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Open Access 01-12-2024 | Alzheimer's Disease | Research

Impact of diabetes mellitus on the risk of Alzheimer’s disease: a mendelian randomization study

Authors: Weichao Wang, Jie Zhang, Man Zhang, Chengyuan Zhang, Huanli Liu, Wanlin Li, Yimeng Fan

Published in: BMC Neurology | Issue 1/2024

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Abstract

Background

The impact of diabetes on the risk of Alzheimer’s disease remains uncertain. This study aimed to explore this issue from multiple perspectives by using the Mendelian randomization (MR) approach.

Methods

Instrumental variables for predicting six diabetic traits (including insulin and blood glucose), eight metabolic risk factors for diabetes (including total cholesterol and blood pressure), and seven diabetic genes were extracted from their summary data. These data were derived from multiple European cohorts and included 31,684 to 810,865 subjects respectively. The two-sample MR, multivariate MR, and summary-data-based Mendelian randomization (SMR) methods were employed to determine the associations of these traits or genes with the risk of Alzheimer’s disease.

Results

The two-sample MR showed that elevated fasting insulin and total cholesterol levels were associated with an increased risk of dementia in Alzheimer’s disease (P = 0.022, P = 0.041). Elevated systolic and diastolic blood pressure levels were associated with a decreased risk of dementia in Alzheimer’s disease (P = 0.036, P = 0.025). The multivariate MR reported that adjusting for telomere length (a well-established biomarker of aging) did not change these findings (P < 0.05). Additionally, the two-sample MR showed that type 1 and type 2 diabetes did not affect the risk of Alzheimer’s disease. The SMR also indicated that the diabetic genes did not affect the risk of this disease.

Conclusion

Multiple MR approaches concluded that fasting insulin, total cholesterol, and blood pressure, rather than diabetes, were potential metabolic variables that had an impact on the risk of Alzheimer’s disease. However, aging might not be involved in these correlations.
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Literature
1.
2.
Song C, Shi J, Zhang P, Zhang Y, Xu J, Zhao L, et al. Immunotherapy for Alzheimer’s disease: targeting β-amyloid and beyond. Transl Neurodegener. 2022;11:18.CrossRefPubMedPubMedCentral
3.
Burillo J, Marqués P, Jiménez B, González-Blanco C, Benito M, Guillén C. Insulin Resistance and diabetes Mellitus in Alzheimer’s Disease. Cells. 2021;10:1236.CrossRefPubMedPubMedCentral
4.
Wang KC, Woung LC, Tsai MT, Liu CC, Su YH, Li CY. Risk of Alzheimer’s disease in relation to diabetes: a population-based cohort study. Neuroepidemiology. 2012;38:237–44.CrossRefPubMed
5.
Noguchi-Shinohara M, Yuki-Nozaki S, Abe C, Mori A, Horimoto M, Yokogawa M, et al. Diabetes Mellitus, elevated hemoglobin A1c, and Glycated Albumin Are Associated with the Presence of all-cause dementia and Alzheimer’s Disease: the JPSC-AD study. J Alzheimers Dis. 2022;85:235–47.CrossRefPubMed
6.
DiPietro NA. Methods in epidemiology: observational study designs. Pharmacotherapy. 2010;30:973–84.CrossRefPubMed
7.
Xue H, Zeng L, Liu S. Unraveling the link: exploring the causal relationship between diabetes, multiple sclerosis, migraine, and Alzheimer’s disease through mendelian randomization. Front Neurosci. 2023;17:1233601.CrossRefPubMedPubMedCentral
8.
Litkowski EM, Logue MW, Zhang R, Charest BR, Lange EM, Hokanson JE, et al. Mendelian randomization study of diabetes and dementia in the million veteran program. Alzheimers Dement. 2023;19:4367–76.CrossRefPubMed
9.
Meng L, Wang Z, Ji HF, Shen L. Causal association evaluation of diabetes with Alzheimer’s disease and genetic analysis of antidiabetic drugs against Alzheimer’s disease. Cell Biosci. 2022;12:28.CrossRefPubMedPubMedCentral
10.
Li C, Qian H, Feng L, Li M. Causal Association between Type 2 diabetes Mellitus and Alzheimer’s Disease: a two-sample mendelian randomization study. J Alzheimers Dis Rep. 2024;8:945–57.CrossRefPubMedPubMedCentral
11.
Thomassen JQ, Tolstrup JS, Benn M, Frikke-Schmidt R. Type-2 diabetes and risk of dementia: observational and mendelian randomisation studies in 1 million individuals. Epidemiol Psychiatr Sci. 2020;29:e118.CrossRefPubMedPubMedCentral
12.
Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9:2941.CrossRefPubMedPubMedCentral
13.
Chiou J, Geusz RJ, Okino ML, Han JY, Miller M, Melton R, et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature. 2021;594:398–402.CrossRefPubMedPubMedCentral
14.
Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet. 2021;53:840–60.CrossRefPubMedPubMedCentral
15.
Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27:3641–9.CrossRefPubMedPubMedCentral
16.
Mbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet. 2021;53:1097–103.CrossRefPubMed
17.
18.
Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24.CrossRefPubMed
19.
Surendran P, Feofanova EV, Lahrouchi N, Ntalla I, Karthikeyan S, Cook J, et al. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals. Nat Genet. 2020;52:1314–32.CrossRefPubMedPubMedCentral
20.
Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53:1300–10.CrossRefPubMedPubMedCentral
21.
Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18.CrossRefPubMedPubMedCentral
22.
23.
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.CrossRefPubMedPubMedCentral
24.
25.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.CrossRefPubMedPubMedCentral
26.
Greco MFD, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34:2926–40.CrossRef
27.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.CrossRefPubMedPubMedCentral
28.
Zoorob RJ, Kihlberg CJ, Taylor SE. Aging and disease prevention. Clin Geriatr Med. 2011;27:523–39.CrossRefPubMed
29.
Cheng F, Carroll L, Joglekar MV, Januszewski AS, Wong KK, Hardikar AA, et al. Diabetes, metabolic disease, and telomere length. Lancet Diabetes Endocrinol. 2021;9:117–26.CrossRefPubMed
30.
Codd V, Wang Q, Allara E, Musicha C, Kaptoge S, Stoma S, et al. Polygenic basis and biomedical consequences of telomere length variation. Nat Genet. 2021;53:1425–33.CrossRefPubMedPubMedCentral
31.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.CrossRefPubMed
32.
Lee SH, Park SY, Choi CS. Insulin resistance: from mechanisms to therapeutic strategies. Diabetes Metab J. 2022;46:15–37.CrossRefPubMed
33.
Kellar D, Craft S. Brain insulin resistance in Alzheimer’s disease and related disorders: mechanisms and therapeutic approaches. Lancet Neurol. 2020;19:758–66.CrossRefPubMedPubMedCentral
34.
Dunk MM, Driscoll I, Alzheimer’s Disease Neuroimaging Initiative. Total cholesterol and APOE-Related risk for Alzheimer’s Disease in the Alzheimer’s Disease Neuroimaging Initiative. J Alzheimers Dis. 2022;85:1519–28.CrossRefPubMedPubMedCentral
35.
Hall JR, Wiechmann AR, Johnson LA, Edwards M, Barber RC, Cunningham R, et al. Total cholesterol and neuropsychiatric symptoms in Alzheimer’s disease: the impact of total cholesterol level and gender. Dement Geriatr Cogn Disord. 2014;38:300–9.CrossRefPubMed
36.
Van Dijk EJ, Breteler MM, Schmidt R, Berger K, Nilsson LG, Oudkerk M, et al. The association between blood pressure, hypertension, and cerebral white matter lesions: cardiovascular determinants of dementia study. Hypertension. 2004;44:625–30.CrossRefPubMed
37.
Den Heijer T, Launer LJ, Prins ND, van Dijk EJ, Vermeer SE, Hofman A, et al. Association between blood pressure, white matter lesions, and atrophy of the medial temporal lobe. Neurology. 2005;64:263–7.CrossRef
38.
Bakker SL, de Leeuw FE, den Heijer T, Koudstaal PJ, Hofman A, Breteler MM. Cerebral haemodynamics in the elderly: the Rotterdam study. Neuroepidemiology. 2004;23:178–84.CrossRefPubMed
39.
Ruitenberg A, den Heijer T, Bakker SL, Swieten JC, Koudstaal PJ, Hofman A, et al. Cerebral hypoperfusion and clinical onset of dementia: the Rotterdam Study. Ann Neurol. 2005;57:789–94.CrossRefPubMed
40.
Yuan M, Chen SJ, Li XL, Xu LJ. Blood pressure and the risk of Alzheimer’s Disease: is there a link? Am J Alzheimers Dis Other Demen. 2016;31:97–8.CrossRefPubMed
Metadata
Title
Impact of diabetes mellitus on the risk of Alzheimer’s disease: a mendelian randomization study
Authors
Weichao Wang
Jie Zhang
Man Zhang
Chengyuan Zhang
Huanli Liu
Wanlin Li
Yimeng Fan
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Neurology / Issue 1/2024
Electronic ISSN: 1471-2377
DOI
https://doi.org/10.1186/s12883-024-03955-y

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