D. Liu1,2,3,*, A. Baranova4,5,*, F. Zhang6,7
1. Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China, 210008; 2. Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China, 210008; 3. Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China, 210008; 4. School of Systems Biology, George Mason University, Manassas, VA, 20110, USA; 5. Research Centre for Medical Genetics, Moscow, 115478, Russia; 6. Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China; 7. Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China; *These authors are co-first authors.
Corresponding Author: Fuquan Zhang (zhangfq@njmu.edu.cn), Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China, Tel: 862582296593, Fax: 862582296593
J Prev Alz Dis 2024;
Published online July 31, 2024, http://dx.doi.org/10.14283/jpad.2024.148
Abstract
BACKGROUND: Alzheimer’s disease (AD) has a high comorbidity with type 2 diabetes (T2D). However, there is still some controversy over whether T2D has a causal impact on AD at present.
OBJECTIVES: We aimed to reveal whether T2D has a causal effect on AD using large-scale genetic data.
METHODS: Firstly, we performed a primary two-sample Mendelian randomization (MR) analysis to assess the potential causal effects of T2D on AD. For this analysis, we used the largest available genome-wide association studies (GWAS) T2D (T2D1, including 80,154 cases and 853,816 controls) and AD (AD1, including 111,326 cases and 677,663 controls) datasets. Additionally, we performed a validation MR analysis using two largely overlapping-sample datasets from FinnGen, including T2D (T2D2, including 57,698 cases and 308,252 controls) and AD (AD2, including 13,393 cases and 363,884 controls). In all MR analyses, the inverse variance-weighted method was used as the primary analysis method, supplemented by the weighted-median and MR-Egger techniques.
RESULTS: In the primary analysis, we found that T2D was not associated with the risk of AD (OR: 0.98, CI: 0.95-1.01, P=0.241). Similarly, no significant association was detected in the validation MR analysis (OR: 0.97, CI: 0.64-1.47, P=0.884).
CONCLUSION: Our findings provide robust evidence that T2D does not have a causal impact on AD. Future studies need to further explore the effect of T2D on the non-AD components of the dementia phenotype.
Key words: Alzheimer’s disease, mendelian randomization, type 2 diabetes.
Introduction
The previous observational studies suggest that there may be a close association between type 2 diabetes (T2D) and dementia (1, 2). Litkowski et al. reported significant associations between genetic risk scores (GRS) for T2D and all-cause dementia as well as between GRS for T2D and clinically diagnosed vascular dementia. Notably, no associations between GRS for T2D and clinically diagnosed Alzheimer’s disease (AD) (OR 1.02, P = 0.43) were reported (1). Recently, utilizing the same sources, Litkowski and colleagues reported a causal increase in the likelihood of all three types of dementia, including general dementia, AD, and vascular dementia (3). These two studies used similar data resources; however, some results seem inconsistent. In fact, the causal relationship between T2D and AD has remained inconclusive and the subject of ongoing debate. Currently, a controversy surrounding the causal relationship between T2D and AD remains.
An association of diabetes with cerebrovascular pathology, which is one of the co-pathologies of AD, is certain (4); this could be a confounding factor in observational studies. While Alzheimer’s pathophysiology is a major driver of cognitive decline in the aging population, it is not the only one. AD constitutes an independent dementia phenotype, which is clearly defined within the ATN diagnostic framework (4) and is distinguishable by its genetic risk markers at the genomic level (5, 6). Therefore, focused investigations of the relationships between T2D and AD as such are paramount. Inferring causal effects solely from observational studies is challenging due to the presence of confounding variables and limitations in sample size. These limitations may be circumvented by using the Mendelian randomization (MR) approach, which extracts data from genome-wide association studies (GWAS) to create instrumental variables (IVs) that are not subjected to the influence of environmental and clinical confounders. Indeed, the random allocation of alleles to offspring at meiosis allows MR to dissect the causal relationship between exposure and outcome. In this work, we utilize data from several large-scale GWAS to identify genetic proxies for T2D, and then investigate their causal relationship using MR analysis.
Materials and methods
In this study, we attempted to infer a causal relationship between T2D and AD using large-scale genetic data. For the primary two-sample MR analysis, the summary-level genetic data from the largest GWAS for T2D (T2D1, 80,154 cases and 853,816 controls) (7) and AD (AD1, 111,326 cases and 677,663 controls) were obtained (6). We further performed a validation MR analysis using two largely overlapping sample datasets from FinnGen, including T2D (T2D2, 57,698 cases and 308,252 controls) and AD (AD2, 13,393 cases and 363,884 controls) (8). In using overlapping-sample data, there is more potential for detecting the association between an exposure and an outcome. All participants were of European descent and the summary-level data used in this research were publicly accessible. Ethical approvals were obtained in all original studies. The primary analysis was executed using the inverse variance-weighted (IVW) method, supplemented by the weighted-median (WM) and MR-Egger techniques. IVs were selected among single nucleotide polymorphisms (SNPs) with a significance level of P < 5×10−8. The linkage disequilibrium (LD) threshold was set at r2 = 0.001 within a 10,000 kb distance. We assessed SNP heterogeneity using Cochran’s Q test and I2 statistics.
Results
In the two-sample MR analysis of T2D1 and AD1 datasets, a total of 182 IVs were obtained (Figure 1A). In this pair of datasets, T2D was not associated with the risk of AD (OR: 0.98, CI: 0.95-1.01, P=0.241). In the validation MR analysis between T2D2 and AD2 datasets, which yielded a total of 158 IVs (Figure 1B), no causal effect of T2D on AD was detected as well (OR: 0.97, CI: 0.64-1.47, P=0.884). The results obtained using different MR techniques were in line with each other and are shown in Table 1. These results, along with corresponding heterogeneity tests, are also available in Supplementary Material Table 1.
Figure 1. The scatter plot illustrates the association between T2D and AD
Different colored lines represent various MR analysis methods, including inverse variance-weighted (IVW), weighted-median (WM), and MR-Egger. A, associations between T2D1 and AD1 in two-sample MR analysis. B, associations between T2D2 and AD2 in validation MR analysis.
Table 1. Causal effect of type 2 diabetes on Alzheimer’s disease
Note: T2D, type 2 diabetes; AD: Alzheimer’s disease; IVW, inverse variance-weighted; WM, weighted-median; N-IV: number of instrumental variables; OR: odds ratio; CI: confidence interval.
Conclusion
In conclusion, according to the primary and validation MR analyses, T2D does not have a causal impact on AD. Currently, the definition of AD has gradually transitioned from being based solely on clinical symptoms to a biologically defined diagnostic framework (9). Reliance on a set of clinical symptoms or a single biomarker is insufficient for an AD diagnosis (9, 10). The etiological mechanisms of vascular dementia differ from those of AD, yet it has a high prevalence and comorbidity rate with AD in the elderly (11). This poses a challenge for early dementia-related studies to accurately screen and analyze pure AD patients, potentially contributing to the inconsistent findings observed in current research on the relationship between T2D and AD, encompassing both observational and MR studies. Our two-sample MR analysis utilized the largest available T2D and AD GWAS datasets and found no significant causal relationship between the two phenotypes. This is a robust finding, which is consistent with the current neuropathological perspective, pointing at a lack of association between diabetes and AD-related neuropathology (12).
Observational studies to date have consistently demonstrated a relationship between T2D and an increased risk of dementia. However, the associations are notably stronger for vascular dementia when compared to AD (13). As MR-based evaluation of associations is more accurate than observational studies, as they are less influenced by socioeconomic (14) and environmental factors (15, 16), we believe that future studies of T2D/dementia intersect should concentrate on exploring the impact of T2D on vascular dementia. These findings suggest that treatment strategies for T2D may only be effective in preventing non-AD-related dementia or cognitive impairment. This further underscores the importance of assessing dementia etiology and AD pathology in elderly patients with multiple comorbidities before devising individual cognitive care or treatment plans.
Ethics approval and consent to participate: All data sources used in this MR study received approval from an ethics standards committee on human experimentation and obtained informed consent from all participants.
Consent for publication: Not applicable.
Availability of data and materials: All GWAS summary datasets used in this study are publicly available for download by qualified researchers (https://gwas.mrcieu.ac.uk/ & https://r9.finngen.fi/).
Competing interests: The authors declare no conflicts of interest.
Funding: This work was supported by the Jiangsu Funding Program for Excellent Postdoctoral Talent (2023ZB184) and the China Postdoctoral Science Foundation (2023M741648).
Authors’ contributions: Dongming Liu: Writing and creating graphical representations. Ancha Baranova: Writing and manuscript refinement. Fuquan Zhang: Data collection, research design, and data analysis.
Acknowledgments: We thank all participants and investigators who contributed to the datasets analyzed.
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