To investigate the potential progression rate of cerebral small vessel disease (CSVD) in brain MRI among neurologically asymptomatic middle-aged individuals with type 1 diabetes.
A total of 172 individuals with type 1 diabetes were re-examined with brain MRI 7.5 years after their initial visit. Baseline predictors of changes in CSVD, particularly cerebral microbleeds (CMBs) and white matter hyperintensities (WMHs) were analyzed.
The proportion of individuals with type 1 diabetes with CSVD increased from 36% to 70% (P < 0.001), CMBs increased from 17% to 33% (P < 0.001), and WMHs from 23% to 63% (P < 0.001) at follow-up. The increase in CMBs was associated with baseline systolic blood pressure, HbA1c, and preexisting CMBs. The increases in CSVD and WMHs were associated only with age.
The prevalence of CSVD doubled over a 7.5-year period in middle-aged individuals with type 1 diabetes.
Introduction
We have demonstrated a high prevalence of cerebral small vessel disease (CSVD), particularly cerebral microbleeds (CMBs) and white matter hyperintensities (WMHs), in middle-aged individuals with type 1 diabetes who lack overt neurological signs or symptoms (1). At baseline, systolic blood pressure, nocturnal blood pressure, and diabetic eye disease were independently associated with CMBs, whereas age was independently associated with WMHs (1–3). Here, our aim was to investigate whether these MRI findings change over time in the same individuals and identify factors contributing to these potential changes.
Research Design and Methods
This study derives from the prospective Finnish Diabetic Nephropathy (FinnDiane) Study described earlier (4). Between 2011 and 2017, 191 participants with type 1 diabetes were enrolled in this study assessing the presence of covert cerebrovascular disease (1). Participants were aged 18–50 years and had no signs or symptoms of neurological or cardiovascular disease or contraindications to MRI. We excluded one individual who had a history of a neurosurgical intervention, two because of multiple sclerosis, and one because of diffuse axonal injury. The remaining 187 were invited for a follow-up visit and 172 (92%) participated. There were no differences in any clinical characteristics between those included and excluded from the initial cohort (data not shown).
The study follows the Declaration of Helsinki, with approval from the Ethics Committee of Helsinki and Uusimaa Hospital District. Written informed consent was obtained from all participants.
Clinical and laboratory assessments were done at the Helsinki FinnDiane research unit at baseline. These encompassed anthropometrics, blood pressure (an average of two blood pressure measurements was used), medical history, and lifestyle queries. Levels of plasma creatinine, lipids, lipoproteins, and hs-CRP were measured, as was HbA1c.
Albuminuria was defined as increased albumin excretion rate (≥20 μg/min or ≥30 mg/24 h) in two of three urine sample collections. The Chronic Kidney Disease Epidemiology Collaboration formula (5) was used to estimate glomerular filtration rate. Retinal fundus photographs were taken at baseline in 87% of those with type 1 diabetes to diagnose and grade diabetic retinopathy (DR) (6) according to the Early Treatment Diabetic Retinopathy Study (ETDRS) severity scale (7). Any DR was defined as an ETDRS score of ≥20 and proliferative diabetic retinopathy (PDR) as ≥61. An experienced ophthalmologist (P.A.S.) evaluated fundus images of both eyes. The eye showing more severe retinal changes was used in the analysis.
From 2011 to 2017, brain MRI (3T Achieva, Philips Ingenia, the Netherlands) was performed at Helsinki University Hospital's Medical Imaging Center within a year of the baseline clinical assessments. MRI sequences included T1, T2, fluid-attenuated inversion recovery, susceptibility-weighted imaging, T2*, diffusion-weighted imaging, T1 magnetization-prepared rapid gradient echo, and MR time-of-flight. A senior neuroradiologist (J.M.), blinded to clinical data, evaluated MRIs for CMBs, WMHs, and infarcts, using standardized criteria (6,8).
At the follow-up phase (2019–2022), MRI scans were performed using the same sequences as at baseline. Abnormalities were assessed by an experienced neuroradiologist (J.M.) and defined as any CMB or two or more WMH lesions.
Statistical Analysis
Continuous variables were assessed using the Wilcoxon test. Results were reported as medians with interquartile ranges for descriptive statistics. Categorical variables were analyzed using the χ2 test. Logistic regression analyses were applied to explore associations with progression of CMBs and WMHs.
Multivariate binary logistic regression analyses were performed to determine independent associations with the progression of CMBs and WMHs (increase versus no change or a reduction) during follow-up. The results are presented as odds ratios (ORs) with 95% CIs. Because age and diabetes duration are closely correlated, we ran additional models for two groups: individuals diagnosed before the age of 18 years and those diagnosed when they were aged 18 years or older, excluding duration as a parameter.
Analyses were conducted using R (version 4.1.1). Statistical significance was defined as P < 0.05.
Data and Resource Availability
Individual-level data of the study participants are not publicly available because of the restrictions due to the study consent provided by the participants at the time of data collection. Readers may, however, request collaboration with the authors to explore individual-level data by contacting the lead investigator.
Results
Study Population and Progression Rates
Baseline clinical characteristics are presented in Table 1. During follow-up, prevalence of any marker of CSVD increased from 36% to 70% (P < 0.001). The prevalence of CMBs increased from 17% to 33% and as did that of as least two WMHs (from 23 to 63%; both P < 0.001). More detailed brain MRI findings at baseline and at follow-up are presented in Table 2. Progression of CMBs in one patient is visualized in Fig. 1.
Clinical characteristics of participants with type 1 diabetes at baseline (N = 172)
| Characteristic | Median (interquartile range)* |
|---|---|
| Female participants, n (%) | 94 (55) |
| Age, years | 40.6 (33.4–45.3) |
| Diabetes duration, years | 21.8 (18.3–30.9) |
| BMI, kg/m2 | 25.8 (23.8–30.0) |
| Systolic blood pressure, mmHg | 129 (119–139) |
| Diastolic blood pressure, mmHg | 77 (72–82) |
| HbA1c, mmol/mol | 65 (57–72) |
| HbA1c, % | 8.1 (7.4–8.7) |
| Estimated glomerular filtration rate, mL/min/1.73 m2 | 108 (96.8–115) |
| Total cholesterol, mmol/L | 4.4 (4.0–4.9) |
| LDL cholesterol, mmol/L | 2.37 (2.00–2.87) |
| HDL cholesterol, mmol/L | 1.51 (1.27–1.82) |
| Triglycerides, mmol/L | 0.89 (0.69–1.30) |
| High sensitivity C-reactive protein, mg/L | 1.16 (0.52–2.88) |
| Diabetic kidney disease, n (%) | 25 (14.5) |
| Any diabetic retinopathy, n (%) | 134 (78) |
| Proliferative diabetic retinopathy, n (%) | 28 (16) |
| Antihypertensive medication, n (%) | 61 (35.4) |
| Aspirin therapy, n (%) | 14 (8.1) |
| Statin therapy, n (%) | 37 (21.5) |
| Current smoker, n (%) | 13 (7.6) |
| Characteristic | Median (interquartile range)* |
|---|---|
| Female participants, n (%) | 94 (55) |
| Age, years | 40.6 (33.4–45.3) |
| Diabetes duration, years | 21.8 (18.3–30.9) |
| BMI, kg/m2 | 25.8 (23.8–30.0) |
| Systolic blood pressure, mmHg | 129 (119–139) |
| Diastolic blood pressure, mmHg | 77 (72–82) |
| HbA1c, mmol/mol | 65 (57–72) |
| HbA1c, % | 8.1 (7.4–8.7) |
| Estimated glomerular filtration rate, mL/min/1.73 m2 | 108 (96.8–115) |
| Total cholesterol, mmol/L | 4.4 (4.0–4.9) |
| LDL cholesterol, mmol/L | 2.37 (2.00–2.87) |
| HDL cholesterol, mmol/L | 1.51 (1.27–1.82) |
| Triglycerides, mmol/L | 0.89 (0.69–1.30) |
| High sensitivity C-reactive protein, mg/L | 1.16 (0.52–2.88) |
| Diabetic kidney disease, n (%) | 25 (14.5) |
| Any diabetic retinopathy, n (%) | 134 (78) |
| Proliferative diabetic retinopathy, n (%) | 28 (16) |
| Antihypertensive medication, n (%) | 61 (35.4) |
| Aspirin therapy, n (%) | 14 (8.1) |
| Statin therapy, n (%) | 37 (21.5) |
| Current smoker, n (%) | 13 (7.6) |
*Unless otherwise indicated.
MRI findings in participants with type 1 diabetes
| MRI finding | Baseline, n (%) | Follow-up, n (%) | P value |
|---|---|---|---|
| Any marker of CSVD | 62 (36) | 121 (70) | <0.001 |
| At least one CMB | 29 (17) | 57 (33) | <0.001 |
| At least two WMHs | 40 (23) | 108 (63) | <0.001 |
| No. of CMBs* | |||
| 1 | 17 (59) | 27 (47) | 0.103 |
| 2 | 2 (7) | 6 (11) | 0.151 |
| ≥3 | 10 (34) | 24 (42) | 0.011 |
| Topography of CMBs* | |||
| Strictly lobar | 15 (52) | 23 (40) | 0.164 |
| Strictly deep or infratentorial | 5 (17) | 11 (19) | 0.123 |
| Mixed | 9 (31) | 23 (40) | 0.009 |
| MRI finding | Baseline, n (%) | Follow-up, n (%) | P value |
|---|---|---|---|
| Any marker of CSVD | 62 (36) | 121 (70) | <0.001 |
| At least one CMB | 29 (17) | 57 (33) | <0.001 |
| At least two WMHs | 40 (23) | 108 (63) | <0.001 |
| No. of CMBs* | |||
| 1 | 17 (59) | 27 (47) | 0.103 |
| 2 | 2 (7) | 6 (11) | 0.151 |
| ≥3 | 10 (34) | 24 (42) | 0.011 |
| Topography of CMBs* | |||
| Strictly lobar | 15 (52) | 23 (40) | 0.164 |
| Strictly deep or infratentorial | 5 (17) | 11 (19) | 0.123 |
| Mixed | 9 (31) | 23 (40) | 0.009 |
*Percentage among those with CMBs.
A: MRI scan from the baseline showing cerebral microbleeds (CMBs) identified in a single section, marked by yellow arrows. B: The same section as in the MRI scan from follow-up phase. Previously observed CMBs are marked by yellow arrows; new CMBs that appeared during the follow-up are indicated by white arrows.
A: MRI scan from the baseline showing cerebral microbleeds (CMBs) identified in a single section, marked by yellow arrows. B: The same section as in the MRI scan from follow-up phase. Previously observed CMBs are marked by yellow arrows; new CMBs that appeared during the follow-up are indicated by white arrows.
CMBs in Individuals With Type 1 Diabetes
Individuals with CMBs at follow-up had a higher albuminuria rate (20% vs. 7.6%; P < 0.001), more antihypertensive medication use (56% vs. 29%; P < 0.001), and higher systolic blood pressure (135 [131–139] vs. 129 [126–132] mmHg; P < 0.001) at baseline compared with those without CMBs. Systolic blood pressure, HbA1c, and baseline CMBs were independently associated with an increase in CMBs between the two visits, in a binary logistic regression analysis (Table 3). Age was associated with an increase in CMBs (per 1-year increment, OR 1.024 [95% CI 1.017–1.030]; P < 0.001) in individuals diagnosed with type 1 diabetes when younger than 18 years. This was not observed in those diagnosed when they are 18 years old or older (per 1-year increment, OR 1.001 [95% CI 0.982–1.021]; P = 0.891).
Univariable and multivariable binary logistic regression OR analysis on the progression of CMBs among people with type 1 diabetes
| Variable | Univariable model OR (95% CI) | P value | Multivariable model OR (95% CI) | P value |
|---|---|---|---|---|
| Age, per year | 1.019 (1.010–1.028) | <0.001 | 1.006 (0.996–1.019) | 0.179 |
| Diabetes duration, per year | 1.011 (1.005–1.018) | <0.001 | 1.006 (0.999–1.013) | 0.104 |
| BMI, per kg/m2 | 1.019 (1.003–1.035) | 0.025 | 1.003 (0.987–1.020) | 0.717 |
| Systolic blood pressure, per mmHg | 1.009 (1.004–1.013) | <0.001 | 1.005 (1.000–1.010) | 0.040 |
| Diastolic blood pressure, per mmHg | 1.003 (0.995–1.011) | 0.420 | – | – |
| HbA1c, per mmol/mol | 1.009 (1.003–1.014) | 0.002 | 1.007 (1.002–1.013) | 0.006 |
| hs-CRP, per mg/L | 0.992 (0.973–1.012) | 0.452 | – | |
| eGFR, per mL/min | 1.001 (0.996–1.006) | 0.640 | – | – |
| Total cholesterol, per mmol/L | 1.096 (1.007–1.192) | 0.034 | 1.055 (0.971–1.146) | 0.211 |
| HDL cholesterol, per mmol/L | 1.020 (0.860–1.211) | 0.822 | – | |
| LDL cholesterol, per mmol/L | 1.062 (0.971–1.161) | 0.192 | – | – |
| Triglycerides, per mmol/L | 1.138 (1.040–1.245) | 0.005 | 1.003 (0.910–1.105) | 0.951 |
| No. of CMBs at baseline, per n | 1.012 (1.005–1.019) | 0.002 | 1.010 (1.003–1.017) | 0.005 |
| Variable | Univariable model OR (95% CI) | P value | Multivariable model OR (95% CI) | P value |
|---|---|---|---|---|
| Age, per year | 1.019 (1.010–1.028) | <0.001 | 1.006 (0.996–1.019) | 0.179 |
| Diabetes duration, per year | 1.011 (1.005–1.018) | <0.001 | 1.006 (0.999–1.013) | 0.104 |
| BMI, per kg/m2 | 1.019 (1.003–1.035) | 0.025 | 1.003 (0.987–1.020) | 0.717 |
| Systolic blood pressure, per mmHg | 1.009 (1.004–1.013) | <0.001 | 1.005 (1.000–1.010) | 0.040 |
| Diastolic blood pressure, per mmHg | 1.003 (0.995–1.011) | 0.420 | – | – |
| HbA1c, per mmol/mol | 1.009 (1.003–1.014) | 0.002 | 1.007 (1.002–1.013) | 0.006 |
| hs-CRP, per mg/L | 0.992 (0.973–1.012) | 0.452 | – | |
| eGFR, per mL/min | 1.001 (0.996–1.006) | 0.640 | – | – |
| Total cholesterol, per mmol/L | 1.096 (1.007–1.192) | 0.034 | 1.055 (0.971–1.146) | 0.211 |
| HDL cholesterol, per mmol/L | 1.020 (0.860–1.211) | 0.822 | – | |
| LDL cholesterol, per mmol/L | 1.062 (0.971–1.161) | 0.192 | – | – |
| Triglycerides, per mmol/L | 1.138 (1.040–1.245) | 0.005 | 1.003 (0.910–1.105) | 0.951 |
| No. of CMBs at baseline, per n | 1.012 (1.005–1.019) | 0.002 | 1.010 (1.003–1.017) | 0.005 |
Variables significantly associated with progression of CMBs in the univariate analysis were included in the multivariate model. All variables in the models were obtained at baseline.
WMHs in Individuals With Type 1 Diabetes
Individuals with WMH progression were older (median age 40.2 [IQR 33.2–45.0] years vs. 36.7 [34.4–44.3] years; P < 0.001) and had a longer diabetes duration (21.2 [17.7–25.1] years vs. 22.6 [18.8–35.3] years; P = 0.009) at baseline. Only age was associated with progression of WMHs (per 1-year increment, OR 2.25 [95% CI 1.36–3.72]; P = 0.002) after correcting for clinically relevant covariates.
Progression of CMBs and WMHs Among Participants With DR
At baseline, 78% of individuals (n = 134 of 172) with type 1 diabetes had any DR, and 16% (n = 28) had PDR. Progression of CMBs was more common among those with DR and PDR at baseline compared with those without those conditions (DR: 34% vs. 17%, P = 0.004; PDR: 43% vs. 20%, P = 0.010).
Conclusions
The burden of CSVD nearly doubled over a period of 7.5 years among neurologically asymptomatic individuals with type 1 diabetes: overall prevalence increased from 36 to 70%. Progression of CMBs was associated with baseline HbA1c, systolic blood pressure, and number of CMBs at baseline, and with age younger than 18 years at diabetes diagnosis. Progression of WMHs was solely associated with age.
We previously observed no association between markers of glycemic control and CMBs in this population (9). One possible explanation is that a cumulative effect of chronic hyperglycemia on the cerebral arteries was still short at baseline but became evident after 7.5 more years. The increase in CMBs associated with DR and albuminuria (1) may reflect generalized microvascular disease. At baseline, CSVD, particularly CMBs, was associated with the severity of DR (2); now we observed that progression of CMBs was more common among those with DR when compared with those without any form of DR, and also in PDR when compared with individuals without PDR. The association between CMBs and hypertension was observed in our baseline study (1,3) and has been shown in the general population (10).
Key strengths of our study include a strong phenotype, high follow-up rate (92%), and consistent MRI interpretation by the same neuroradiologist. Limitations include the use of different MRI scanners (yet both were 3T scanners from the same manufacturer) lack of total WMH volume measurements, and not having a control group against which to compare the progression rate of CSVD.
In conclusion, the prevalence of CSVD nearly doubled in 7.5 years in middle-aged individuals with type 1 diabetes. The long-term clinical implications of the changes remain uncertain, and interventional studies are necessary to determine whether tighter control of blood pressure and glycemia can slow CMB progression.
Article Information
Acknowledgments. The skilled technical assistance of Anu Dufva, Anna Sandelin, and Kirsi Uljala is gratefully acknowledged. We also gratefully thank Pentti Pölönen, Department of Radiology, Helsinki University Hospital, for performing the MRI scans. We are indebted to Oili Salonen, Department of Radiology, Helsinki University Hospital, for her contribution in planning the MRI protocol. During the course of preparing this work, the authors used ChatGPT for the purpose of fixing grammar. After using this tool/service, the authors formally reviewed the content for its accuracy and edited it as necessary. The authors take full responsibility for all the content of this publication.
Funding. The FinnDiane Study was supported by grants from Folkhälsan Research Foundation, Wilhelm and Else Stockmann Foundation, Liv och Hälsa Society, Medical Society of Finland (Finska Läkaresällskapet), Sigrid Juselius Foundation, Finnish Foundation for Cardiovascular Research, and by State Funding for University-level Health Research (grant TYH2023403). D.G. was supported by the Liv och Hälsa Society, Medical Society of Finland (Finska Läkaresällskapet), Sigrid Juselius Foundation, State Funding for University-level Health Research (grant TYH 2021206), University of Helsinki, Minerva Foundation Institute for Medical Research, and Academy of Finland (grant UAK1021MRI).
None of the funding bodies had any role in the study design; collection, analysis, or interpretation of data; writing of the manuscript; or the decision to submit the manuscript for publication.
Duality of Interest. A.T. is a shareholder and cofounder of RokoteNyt Oy. M.I.E. is a shareholder of BCB Medical. D.G. reports receiving lecture or advisory board honoraria from Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Fresenius, GE Healthcare, Harald AI, Novo Nordisk, and Ratiopharm. J.M. reports receiving lecture honoraria from Santen. P.-H.G. has received lecture honoraria from AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Elo Water, Genzyme, Medscape, Merck Sharp & Dohme (MSD), Mundipharma, Novartis, Novo Nordisk, PeerVoice, Sanofi, and SCIARC, and is an advisory board member of AbbVie, Bayer, Boehringer Ingelheim, Eli Lilly, Janssen, Medscape, MSD, Mundipharma, Novartis, Novo Nordisk, and Sanofi. T.T. is serving or has served as an advisory board member to AstraZeneca, Bayer, Bristol Myers Squibb, Boehringer Ingelheim, Inventiva, and Portola Pharma and received lecture honorarium from Argenx. No other potential conflicts of interest relevant to this article were reported.
Author Contributions. A.T., I.K., L.M.T., J.P., D.G., and J.M. contributed to the study design and acquisition and interpretation of data. A.T. and I.K. had the main responsibility for analyzing the data and writing the first draft of the paper. J.M. evaluated the MR images and, together with L.T. and D.G., compiled the original data set of the study. J.I., M.I.E., L.M.T., P.A.S., T.T., P.-H.G., J.P., and D.G. critically revised the manuscript. L.T. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Prior Presentation. An abstract on this topic was presented at the European Congress of Radiology conference, Vienna, Austria, 1–5 March 2023. At that time, not all participants’ data had been analyzed yet.
Handling Editors. The journal editors responsible for overseeing the review of the manuscript were John B. Buse and M. Sue Kirkman.
