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. 2023 Nov 7;96(2):725–743. doi: 10.3233/JAD-230623

Genetic Insights into the Risk of Metabolic Syndrome and Its Components on Dementia: A Mendelian Randomization

Qiang He a, Wenjing Wang b, Hao Li c, Yang Xiong d, Chuanyuan Tao a,*, Lu Ma a, Chao You a,*
Editor: Liyong Wu
PMCID: PMC10657705  PMID: 37840498

Abstract

Background:

The role of metabolic syndrome (MetS) on dementia is disputed.

Objective:

We conducted a Mendelian randomization to clarify whether the genetically predicted MetS and its components are casually associated with the risk of different dementia types.

Methods:

The genetic predictors of MetS and its five components (waist circumference, hypertension, fasting blood glucose, triglycerides, and high-density lipoprotein cholesterol [HDL-C]) come from comprehensive public genome-wide association studies (GWAS). Different dementia types are collected from the GWAS in the European population. Inverse variance weighting is utilized as the main method, complemented by several sensitivity approaches to verify the robustness of the results.

Results:

Genetically predicted MetS and its five components are not causally associated with the increasing risk of dementia (all p > 0.05). In addition, no significant association between MetS and its components and Alzheimer’s disease, vascular dementia, frontotemporal dementia, dementia with Lewy bodies, and dementia due to Parkinson’s disease (all p > 0.05), except the association between HDL-C and dementia with Lewy bodies. HDL-C may play a protective role in dementia with Lewy bodies (OR: 0.81, 95% CI: 0.72–0.92, p = 0.0010).

Conclusions:

From the perspective of genetic variants, our study provides novel evidence that MetS and its components are not associated with different dementia types.

Keywords: Alzheimer’s disease, causal association, components, dementia, Mendelian randomization, metabolic syndrome, types

INTRODUCTION

Dementia is characterized by a chronic and progressive decline affecting cognitive function in aged adults [1]. Generally, the main types of dementia consist of Alzheimer’s disease (AD), vascular dementia, frontotemporal dementia, dementia with Lewy bodies, and dementia in Parkinson’s disease. It is estimated that there have 50 million patients around the world [2]. More seriously, the number of cases is dramatically increasing due to the increasing life expectancy and risk factors [3], which puts a heavy burden on individuals, families, health care, and society. Therefore, strategies for preventing and alleviating dementia are priorities in healthcare.

Metabolic syndrome (MetS) is a cluster of pathological conditions based on the World Health Organization’s (WHO) definition, including glucose abnormalities, hyperlipidemia, central obesity, and hypertension [4]. At present, the incidence of MetS is increasing rapidly, and approximately 25% adults have MetS [5]. Some studies have shown that MetS has a positive association with the risk of dementia [6, 7], while no association is observed, even the inverse relationship in other studies [8, 9]. In addition, obvious confounding factors such as the study design and retrospective features are inherent shortcomings in these observational studies, which may interfere with the understanding of these conclusions.

Mendelian randomization (MR), as a genetic approach, is a robust statistical analysis using genetic variants to make a causal inference, which can overcome the limitation of observational studies [10]. During gestation, single nucleotide polymorphism (SNP), a genomic variant at a single base position in the deoxyribonucleic acid (DNA), is assorted randomly in forming a zygote [11]. However, no study has been conducted to investigate the causal association of MetS and its five components on dementia. Therefore, we performed this MR analysis to illustrate their causal links.

METHODS

Study design

The overview of our MR study is shown in Fig. 1. In our study, we explored the causal relationship between MetS, waist circumference (WC), hypertension, fasting blood glucose (FBG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and different dementia types, including AD, vascular dementia, frontotemporal dementia, dementia with Lewy bodies, and dementia due to Parkinson’s disease. No ethical approval is required due to the analysis of the public summary-level datasets.

Fig. 1.

Fig. 1

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The flow chart of our MR analysis. MetS, metabolic syndrome; MR, Mendelian randomization; SNP, single nucleotide polymorphism; HDL-C, high-density lipoprotein cholesterol.

Date sources of exposures and outcomes

All exposure datasets are originated from public databases. MetS (N = 291,107 samples), WC (N = 462,166 samples), hypertension (N = 463,010 samples), TG (441,016 samples), and HDL-C (403,943 samples) are obtained from the UK biobank [12, 13]. Genetic predictors for FBG (281,416 participants) are available from the Meta-Analyses Glucose and Insulin-related traits Consortium (MAGIC) [14]. The detailed sources of these datasets utilized in our MR study are described in Table 1.

Table 1.

The R2 and F-statistics for the genetic instruments in the MR analyses

Exposure Outcome No. SNP R 2 F-statistic
Mets Any Dementia 122 3.04% 66.77
WC Any Dementia 561 7.21% 53.67
Hypertension Any Dementia 66 0.85% 46.78
FBG Any Dementia 108 4.37% 101.31
TG Any Dementia 749 17.90% 110.95
HDL-C Any Dementia 900 29.50% 161.96
Mets Alzheimer’s disease 119 3.07% 68.89
WC Alzheimer’s disease 565 7.23% 53.89
Hypertension Alzheimer’s disease 66 0.84% 46.24
FBG Alzheimer’s disease 107 4.33% 102.01
TG Alzheimer’s disease 789 19.77% 118.26
HDL-C Alzheimer’s disease 951 30.53% 159.49
Mets Vascular dementia 124 3.13% 67.69
WC Vascular dementia 564 7.26% 53.76
Hypertension Vascular dementia 66 0.85% 46.78
FBG Vascular dementia 108 4.37% 101.31
TG Vascular dementia 757 18.43% 113.44
HDL-C Vascular dementia 906 29.70% 162.42
Mets Frontotemporal dementia 46 1.27% 75.39
WC Frontotemporal dementia 227 3.14% 57.51
Hypertension Frontotemporal dementia 23 0.27% 50.25
FBG Frontotemporal dementia 32 1.06% 79.91
TG Frontotemporal dementia 199 5.25% 109.23
HDL-C Frontotemporal dementia 237 8.63% 146.18
Mets Dementia with Lewy bodies 114 2.96% 69.56
WC Dementia with Lewy bodies 516 6.66% 53.54
Hypertension Dementia with Lewy bodies 63 0.82% 47.14
FBG Dementia with Lewy bodies 101 4.20% 102.97
TG Dementia with Lewy bodies 698 18.16% 121.05
HDL-C Dementia with Lewy bodies 831 27.99% 161.51
Mets Dementia due to Parkinson’s disease 125 3.19% 68.12
WC Dementia due to Parkinson’s disease 565 7.28% 53.88
Hypertension Dementia due to Parkinson’s disease 66 0.85% 46.78
FBG Dementia due to Parkinson’s disease 108 4.37% 101.31
TG Dementia due to Parkinson’s disease 758 18.74% 115.63
HDL-C Dementia due to Parkinson’s disease 907 29.90% 163.85
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MetS, metabolic syndrome; WC, waist circumference; FBG, fasting blood glucose; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol.

All outcome datasets are derived from European ancestry. The summary-level dataset for AD are taken from the MR study including 954 cases and 487,331 controls [15]. The dataset for vascular dementia is extracted from the FinnGen consortium, consisting of 212,389 samples (881 cases and 211,508 controls). As to frontotemporal dementia, its dataset includes 515 cases and 2,509 controls [16]. Summary statistics for dementia with Lewy bodies are collected from an independent GWAS multicenter study with 2,591 cases and 4,027 controls [17]. Dementia due to Parkinson’s disease consists of 212,389 samples (267 cases and 216,628 controls) from the FinnGen consortium. The detailed resources of our datasets are visualized in Table 1.

Genetic instrument selection

Genetic instruments are usually collected as those having statistically robust associations with the risk factor in a MR analysis [18]. The genetic instrument selection undertaken the following procedures. All the genetic instrumental variables (IVs) associated with MetS and its five components must meet a significance level at a genome-wide statistical threshold of p < 5×10–8. Then, the independent SNPs are identified using the linkage disequilibrium (LD) with the threshold of LD r2 < 0.05 at a window size of 10,000 Kb [19, 20]. In addition, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis is used to detect the potential outlier SNPs accounting for possible pleiotropy [21]. The SNPs will be removed when the outlier SNPs are detected. The qualified SNPs of MetS and its five components are displayed in Table 1.

Main statistical analyses

The inverse variance weighting (IVW) approach is deemed as the main method in our MR study because it can obtain a robust result by integrating the Wald ratio of each SNP into an overall weighted effect [22]. The Bonferroni-corrected p < 0.0013 (0.05/36) is regarded as the statistical significance. All analyses are performed using R packages including “TwoSampleMR”, “mr.raps”, and “cause”, in R software (version: 4.1.2, The R Foundation, Vienna, Austria).

Sensitivity analyses

We also chosen five methods to perform sensitivity analyses, including MR robust adjusted profile score (MR.RAPS), MR-PRESSO, weighted median, MR-Egger, and Maximum likelihood. When there were weak IVs that led to horizontal pleiotropy, the results of MR.RAPS could remain stable [23]. Significant outliers could be detected using MR-PRESSO and then removed for pleiotropy [21]. The weighted median approach could obtain consistent results even though 50% of SNPs were invalid [24]. The results of the comparison between the egger intercept term and zero were introduced in MR-Egger analysis, which represented the directional pleiotropy [25]. In the maximum likelihood analysis, a relatively low standard error existed, and it might be deviated by a small sample [26]. Furthermore, the egger intercept term in MR-Egger analysis and the p value in MR-PRESSO analysis were introduced into the regression model to test the directional pleiotropy. Cochran’s Q test was performed to identify possible heterogeneity. In addition, leave-one-out analysis was utilized to explain the robustness of the results when removing SNPs in turn.

RESULTS

The casual effects of genetically predicted MetS and its components on dementia

The results of this MR study are presented in Table 2. The demographic characteristics for dementia are displayed in Tables 3–5.

Table 2.

The causal effect of MetS and its components on different types of dementia

Exposure Outcome Methods OR (95%) p Egger_intercept p-Egger intercept Cochran’s Cochran’s p
MetS Any Dementia IVW 0.98 (0.92,1.06) 0.7564 128.71 0.2985
MR-Egger 0.90 (0.77,1.07) 0.2574 0.0062 0.2678 127.40 0.3046
Weighted median 0.95 (0.85,1.05) 0.3646
Maximum likelihood 0.98 (0.92,1.06) 0.7537
RAPS 0.97 (0.90,1.05) 0.5708
WC Any Dementia IVW 1.07 (0.93,1.22) 0.3128 562.58 0.4613
MR-Egger 1.48 (0.99,2.22) 0.0544 –0.0051 0.0923 559.73 0.4832
Weighted median 1.11 (0.87,1.41) 0.3627
Maximum likelihood 1.07 (0.93,1.22) 0.3086
RAPS 1.04 (0.91,1.21) 0.5050
Hypertension Any Dementia IVW 0.97 (0.31,3.02) 0.9590 69.91 0.3159
MR-Egger 1.28 (0.02,5.46) 0.9048 –0.0015 0.8890 69.89 0.2862
Weighted median 1.48 (0.28,7.79) 0.6416
Maximum likelihood 0.97 (0.32,2.93) 0.9574
RAPS 1.00 (0.31,3.16) 0.9975
FBG Any Dementia IVW 1.26 (1.01,1.57) 0.0394 111.27 0.3444
MR-Egger 1.08 (0.72,1.60) 0.7039 0.0045 0.3525 111.36 0.3417
Weighted median 1.29 (0.91,1.84) 0.1474
Maximum likelihood 1.27 (1.02,1.58) 0.0321
RAPS 1.23 (0.97,1.57) 0.0780
TG Any Dementia IVW 0.94 (0.86,1.02) 0.1481 829.31 0.0202
MR-Egger 0.87 (0.76,0.99) 0.0470 0.0024 0.1628 827.15 0.0216
Weighted median 0.93 (0.80,1.07) 0.3398
Maximum likelihood 0.94 (0.87,1.01) 0.1300
RAPS 0.94 (0.86,1.02) 0.1799
HDL-C Any Dementia IVW 1.04 (0.97,1.12) 0.1751 931.91 0.2169
MR-Egger 1.03 (0.93,1.15) 0.4753 0.0002 0.8435 931.87 0.2103
Weighted median 0.96 (0.85,1.09) 0.6065
Maximum likelihood 1.04 (0.98,1.11) 0.1693
RAPS 1.03 (0.96,1.10) 0.3650
MetS Alzheimer’s disease IVW 1.00 (0.99,1.00) 0.9354 107.68 0.7415
MR-Egger 0.99 (0.99,1.00) 0.8255 6.82e-06 0.7737 107.60 0.7215
Weighted median 1.00 (0.99,1.00) 0.7967
Maximum likelihood 1.00 (0.99,1.00) 0.9354
RAPS 1.00 (0.99,1.00) 0.9205
WC Alzheimer’s disease IVW 1.00 (0.99,1.00) 0.0628 563.75 0.4949
MR-Egger 1.00 (1.00,1.00) 0.0143 –2.61e-05 0.0534 560.01 0.5276
Weighted median 1.00 (1.00,1.00) 0.0227
Maximum likelihood 1.00 (0.99,1.00) 0.0628
RAPS 1.00 (0.99,1.00) 0.0875
Hypertension Alzheimer’s disease IVW 1.00 (0.99,1.01) 0.1914 57.11 0.7461
MR-Egger 1.01 (0.99,1.02) 0.2078 –4.51e-05 0.3491 56.22 0.7445
Weighted median 1.00 (0.99,1.01) 0.4181
Maximum likelihood 1.00 (0.99,1.01) 0.1914
RAPS 1.00 (0.99,1.01) 0.2233
FBG Alzheimer’s disease IVW 1.00 (0.99,1.00) 0.5977 104.17 0.5318
MR-Egger 1.00 (0.99,1.00) 0.4385 –1.24e-05 0.5615 103.84 0.5136
Weighted median 1.00 (0.99,1.00) 0.4037
Maximum likelihood 1.00 (0.99,1.00) 0.5989
RAPS 1.00 (0.99,1.00) 0.6898
TG Alzheimer’s disease IVW 0.99 (0.99,1.00) 0.7071 824.29 0.1795
MR-Egger 1.00 (0.99,1.00) 0.6987 –5.82e-06 0.4371 823.65 0.1770
Weighted median 1.00 (0.99,1.00) 0.9353
Maximum likelihood 0.99 (0.99,1.00) 0.7023
RAPS 0.99 (0.99,1.00) 0.8574
HDL-C Alzheimer’s disease IVW 1.00 (0.99,1.00) 0.9896 974.46 0.2837
MR-Egger 1.00 (0.99,1.00) 0.9293 –6.79e-07 0.9170 974.45 0.2761
Weighted median 1.00 (0.99,1.00) 0.9875
Maximum likelihood 1.00 (0.99,1.00) 0.9895
RAPS 0.99 (0.99,1.00) 0.9850
MetS Vascular dementia IVW 1.05 (0.89,1.24) 0.5115 121.13 0.5305
MR-Egger 1.04 (0.73,1.50) 0.7929 0.0004 0.9726 121.13 0.5050
Weighted median 1.18 (0.91,1.53) 0,1970
Maximum likelihood 1.05 (0.89,1.24) 0.5076
RAPS 1.06 (0.89,1.25) 0.4906
WC Vascular dementia IVW 1.30 (0.94,1.79) 0.1028 539.81 0.7520
MR-Egger 1.32 (0.50,3.43) 0.5652 –0.0002 0.9757 539.81 0.7424
Weighted median 1.26 (0.70,2.24) 0.4304
Maximum likelihood 1.31 (0.95,1.81) 0.0955
RAPS 1.27 (0.91,1.78) 0.1516
Hypertension Vascular dementia IVW 4.17 (0.23,75.75) 0.3336 80.22 0.0966
MR-Egger 1.56 (4.71e-05,51732.67) 0.9332 0.0055 0.8476 80.18 0.0833
Weighted median 12.82 (0.26,628.59) 0.1987
Maximum likelihood 4.37 (0.31,61.56) 0.2740
RAPS 6.69 (0.32,136.52) 0.2165
FBG Vascular dementia IVW 1.09 (0.63,1.87) 0.7435 114.96 0.2819
MR-Egger 0.67 (0.26,1.76) 0.4275 0.0137 0.2389 113.46 0.2922
Weighted median 1.16 (0.51,2.63) 0.7138
Maximum likelihood 1.09 (0.64,1.84) 0.7362
RAPS 1.06 (0.60,1.87) 0.8399
TG Vascular dementia IVW 0.97 (0.81,1.17) 0.8242 737.33 0.6796
MR-Egger 0.92 (0.68,1.24) 0.6011 0.0019 0.6250 737.10 0.6726
Weighted median 1.14 (0.82,1.59) 0.4237
Maximum likelihood 0.97 (0.81,1.17) 0.8251
RAPS 1.01 (0.83,1.22) 0.9121
HDL-C Vascular dementia IVW 0.93 (0.79,1.09) 0.3940 838.12 0.9448
MR-Egger 0.93 (0.72,1.19) 0.5739 8.37e-05 0.9807 838.12 0.9421
Weighted median 0.81 (0.61,1.07) 0.1495
Maximum likelihood 0.93 (0.79,1.09) 0.3960
RAPS 0.93 (0.79,1.10) 0.4526
MetS Frontotemporal dementia IVW 1.26 (0.81,1.95) 0.2926 67.29 0.0172
MR-Egger 2.76 (0.85,8.94) 0.0965 –0.0535 0.1667 64.39 0.0240
Weighted median 1.55 (0.89,2.71) 0.1155
Maximum likelihood 1.26 (0.88,1.81) 0.1929
RAPS 1.31 (0.82,2.06) 0.2470
WC Frontotemporal dementia IVW 0.88 (0.45,1.73) 0.7227 235.42 0.3196
MR-Egger 0.70 (0.10,4.65) 0.7140 0.0031 0.8267 235.37 0.3038
Weighted median 1.00 (0.31,3.20) 0.9941
Maximum likelihood 0.88 (0.45,1.71) 0.7160
RAPS 0.84 (0.42,1.69) 0.6413
Hypertension Frontotemporal dementia IVW 9.15 (0.01,7.77e+03) 0.5200 28.00 0.1756
MR-Egger 1.95e+08 (0.001,2.35e+19) 0.1573 –0.0925 0.1937 25.78 0.2146
Weighted median 9.66e+02 (0.13,6.86e+06) 0.1287
Maximum likelihood 1.01e+01 (0.02,4.33e+03) 0.4533
RAPS 3.37e+01 (0.02,4.03e+04) 0.3305
FBG Frontotemporal dementia IVW 0.49 (0.08,2.98) 0.4446 39.37 0.1437
MR-Egger 0.20 (0.01,32.55) 0.5440 0.0174 0.7153 39.19 0.1213
Weighted median 1.49 (0.13,16.66) 0.7417
Maximum likelihood 0.50 (0.10,2.50) 0.4034
RAPS 0.58 (0.09,3.43) 0.5492
TG Frontotemporal dementia IVW 1.40 (0.86,2.28) 0.1675 220.16 0.1338
MR-Egger 2.45 (1.08,5.52) 0.0317 –0.0164 0.0975 217.11 0.1552
Weighted median 1.61 (0.76,3.42) 0,2079
Maximum likelihood 1.40 (0.88,2.23) 0.1450
RAPS 1.47 (0.91,2.37) 0.1135
HDL-C Frontotemporal dementia IVW 0.91 (0.61,1.35) 0.6463 244.24 0.3424
MR-Egger 0.75 (0.40,1.42) 0.3888 0.0060 0.4596 243.67 0.3350
Weighted median 0.65 (0.33,1.29) 0.2209
Maximum likelihood 0.90 (0.61,1.34) 0.6380
RAPS 0.87 (0.58,1.31) 0.5139
MetS Dementia with Lewy bodies IVW 1.15 (1.01,1.30) 0.0252 114.09 0.4533
MR-Egger 1.19 (0.90,1.59) 0.2149 0.0098 0.7750 114.01 0.4292
Weighted median 1.21 (1.01,1.46) 0.0422
Maximum likelihood 1.15 (1.01,1.31) 0.0242
RAPS 1.14 (0.99,1.31) 0.0530
WC Dementia with Lewy bodies IVW 0.94 (0.73,1.21) 0.6346 522.79 0.3965
MR-Egger 0.85 (0.40,1.80) 0.6814 0.0014 0.7902 522.72 0.3854
Weighted median 0.98 (0.64,1.51) 0.9278
Maximum likelihood 0.94 (0.73,1.21) 0.6445
RAPS 0.96 (0.73,1.25) 0.7733
Hypertension Dementia with Lewy bodies IVW 1.02 (9.75e-02,10.83) 0.9817 86.68 0.0209
MR-Egger 0.04 (7.97e-06,273.56) 0.4914 0.0170 0.4706 85.94 0.0193
Weighted median 1.74 (8.96e-02,34.00) 0.7129
Maximum likelihood 1.02 (1.36e-01,7.76) 0.9781
RAPS 1.02 (8.49e-02,12.36) 0.9845
FBG Dementia with Lewy bodies IVW 1.50 (1.01,2.24) 0.0423 94.59 0.6339
MR-Egger 1.13 (0.56,2.30) 0.7173 0.0080 0.3467 93.69 0.6316
Weighted median 1.19 (0.63,2.26) 0.5789
Maximum likelihood 1.51 (1.01,2.25) 0.0420
RAPS 1.49 (0.98,2.25) 0.0561
TG Dementia with Lewy bodies IVW 1.07 (0.93,1.23) 0.3290 748.16 0.0875
MR-Egger 1.04 (0.83,1.31) 0.7078 0.0009 0.7670 748.06 0.0838
Weighted median 0.99 (0.78,1.25) 0.9756
Maximum likelihood 1.07 (0.93,1.23) 0.3144
RAPS 1.05 (0.91,1.22) 0.4502
HDL-C Dementia with Lewy bodies IVW 0.81 (0.72,0.92) 0.0010 836.34 0.4318
MR-Egger 0.71 (0.59,0.87) 0.0007 0.0045 0.0918 833.48 0.4497
Weighted median 0.78 (0.63,0.97) 0.0257
Maximum likelihood 0.81 (0.72,0.92) 0.0010
RAPS 0.82 (0.72,0.93) 0.0026
MetS Dementia due to Parkinson’s disease IVW 0.84 (0.63,1.12) 0.2546 125.51 0.4451
MR-Egger 0.60 (0.31,1.14) 0.1270 0.0254 0.2527 124.17 0.4532
Weighted median 0.74 (0.48,1.12) 0.1600
Maximum likelihood 0.84 (0.63,1.12) 0.2517
RAPS 0.84 (0.62,1.13) 0.2661
WC Dementia due to Parkinson’s disease IVW 0.65 (0.36,1.17) 0.1542 592.46 0.1966
MR-Egger 0.72 (0.12,4.04) 0.7112 –0.0015 0.9092 592.45 0.1886
Weighted median 0.91 (0.36,2.29) 0.8494
Maximum likelihood 0.66 (0.37,1.17) 0.1628
RAPS 0.68 (0.37,1.24) 0.2105
Hypertension Dementia due to Parkinson’s disease IVW 0.05 (4.11e-04,6.36) 0.2270 70.94 0.2861
MR-Egger 0.01 (2.04e-10,2.09e+05) 0.5704 0.0115 0.8087 70.88 0.2590
Weighted median 0.02 (2.44e-05,28.70) 0.3137
Maximum likelihood 0.04 (4.51e-04,5.13e) 0.2029
RAPS 0.01 (1.09e-04,1.77e) 0.0841
FBG Dementia due to Parkinson’s disease IVW 1.79 (0.71,4.48) 0.2100 96.64 0.7536
MR-Egger 1.14 (0.22,5.84) 0.8744 0.0130 0.5124 96.21 0.7414
Weighted median 1.89 (0.43,8.33) 0.3962
Maximum likelihood 1.79 (0.71,4.50) 0.2113
RAPS 1.81 (0.70,4.68) 0.2165
TG Dementia due to Parkinson’s disease IVW 1.01 (0.73,1.39) 0.9441
MR-Egger 0.99 (0.59,1.68) 0.9900 0.0004 0.9442
Weighted median 1.25 (0.71,2.23) 0.4296
Maximum likelihood 1.01 (0.73,1.39) 0.9444
RAPS 1.01 (0.72,1.41) 0.9389
HDL-C Dementia due to Parkinson’s disease IVW 1.07 (0.80,1.42) 0.6446 982.43 0.0389
MR-Egger 1.67 (1.07,2.63) 0.0239 –0.0162 0.0107 975.39 0.0516
Weighted median 1.45 (0.86,2.45) 0.1621
Maximum likelihood 1.07 (0.81,1.41) 0.6326
RAPS 1.09 (0.81,1.47) 0.5292
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MetS, metabolic syndrome; WC, waist circumference; FBG, fasting blood glucose; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; IVW, inverse-variance weighted; RAPS, robust adjusted profile score; OR, odds ratio.

Table 3.

The demographic characteristics for any dementia, vascular dementia, dementia due to Parkinson’s disease

Exposure Female Male Mean age at first event (year-old) Absolute risk (15 years)
Any dementia 4,281 5,441 77.53 0.02
Vascular dementia 567 1,035 78.53 0.01
Dementia due to Parkinson’s disease 128 262 75.53 –
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Table 4.

The demographic characteristics for frontotemporal dementia

Exposure Female Male Mean age of onset (year-old) Mean age of death (year-old) Motor neuron disease (present) Family history
Frontotemporal dementia 227 286 59.8 67.6 104 169
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Table 5.

The demographic characteristics for dementia with Lewy bodies

Exposure Female Male Clinically ascertained Pathologically diagnosed Mean age (year-old)
Dementia with Lewy bodies 948 1,643 802 1,789 75
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As to any dementia, it can be found that MetS, WC, hypertension, FBG, TG, and HDL-C are not causally associated with the risk of any dementia (all p > 0.0016, Table 2, Fig. 2). The results of Cochran’s Q analysis show a visible heterogeneity between TG and any dementia (Table 2), while a symmetry of MR results in the funnel plot (Fig. 3) is observed. In the MR-Egger and MR-PRESSO analyses, no pleiotropy is identified (MR-Egger: all p > 0.05; MR-PRESSO: all p > 0.05, Table 2). Additionally, no influential SNPs are detected in the leave-one-out analysis when excluding any one of the SNP in turn (Fig. 4). Figure 5 presents the results of the causal estimate of every SNP on any dementia.

Fig. 2.

Fig. 2

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The scatter plots of the association between genetically predicted MetS and its components on dementia in the MR analysis. MetS, metabolic syndrome; WC, waist circumference; FBG, fasting blood glucose; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; AD, Alzheimer’s disease; VD, vascular dementia; FD, frontotemporal dementia; DLB, dementia with Lewy bodies; D-PD, dementia due to Parkinson’s disease.

Fig. 3.

Fig. 3

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The funnel plots of the association between genetically predicted MetS and its components on dementia in the MR analysis. AD, Alzheimer’s disease; VD, vascular dementia; FD, frontotemporal dementia; DLB, dementia with Lewy bodies; HDL-C, high-density lipoprotein cholesterol; MR, Mendelian randomization; D-PD, dementia due to Parkinson’s disease.

Fig. 4.

Fig. 4

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The leave-one-out analysis of the association between genetically MetS and its components on dementia in the MR analysis. AD, Alzheimer’s disease; VD, vascular dementia; FD, frontotemporal dementia; DLB, dementia with Lewy bodies; HDL-C, high-density lipoprotein cholesterol; MR, Mendelian randomization; D-PD, dementia due to Parkinson’s disease.

Fig. 5.

Fig. 5

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The frost plots of the association between genetically MetS and its components on dementia in the MR analysis. AD, Alzheimer’s disease; VD, vascular dementia; FD, frontotemporal dementia; DLB, dementia with Lewy bodies; HDL-C, high-density lipoprotein cholesterol; MR, mendelian randomization; D-PD, dementia due to Parkinson’s disease.

For AD, the results of IVW method show that no causal relationship of MetS and its subtypes is identified (all p > 0.0016, Table 2, Fig. 2). No evidence of heterogeneity is detected in Cochran’s Q analysis (all p > 0.05, Table 2) and the funnel plot (Fig. 3). Furthermore, no signs of pleiotropy is found in MR-Egger and MR-PRESSO analyses (Table 2). The leave-one-out analyses indicate the robustness of our MR results (Fig. 4). The causal estimate of each IV on AD is shown in Fig. 5.

In MR analysis for vascular dementia, we do not observe significant causal association between MetS, its subtypes, and vascular dementia (all p > 0.0016, Table 2, Fig. 2). In sensitivity analysis, Cochran’s Q test does not find any heterogeneity (Fig. 3, Table 2). In addition, there is no evidence of pleiotropyin MR-Egger and MR-PRESSO analyses (Table 2). The causal estimates are not driven by single SNP in the leave-one-out analysis (Fig. 4, Table 2). The frost plot manifesting the casual estimate of every SNP on vascular dementia is shown in Fig. 5.

As to frontotemporal dementia, there is no causal association between MetS, WC, hypertension, FBG, TG, HDL-C, and frontotemporal dementia (all p > 0.0016, Table 2, Fig. 2). Although the results in Cochran’s Q test demonstrate a visible heterogeneity between MetS and frontotemporal dementia (Table 2), the funnel plot reveals a symmetry of MR results (Fig. 3). We do not find pleiotropy in MR-Egger and MR-PRESSO analyses (Table 2), and the results of leave-one-out analysis remain robust (Fig. 4, Table 2). The causal estimate of each IV on frontotemporal dementia is displayed in frost plot (Fig. 5).

HDL-C decreases the risk of dementia with Lewy bodies (odd ratios (OR) = 0.81, 95% confidential index (CI) = 0.72–0.92, p = 0.0010), while no causal relationship is observed between MetS, WC, hypertension, FBG, TG, and dementia with Lewy bodies (all p > 0.0016, Table 2, Fig. 2). The funnel plot is symmetrical despite a visible heterogeneity in Cochran’s Q analysis (Table 2, Fig. 3). MR-Egger method and MR-PRESSO do not find potential pleiotropy (Table 2). The results of the leave-one-out analysis are stable (Fig. 4). The causal estimate of each SNP on dementia with Lewy bodies is depicted in Fig. 5.

As shown in Table 2 and Fig. 2, MetS and its five components are not causally related to dementia due to Parkinson’s disease (all p > 0.05). In sensitivity analyses, although there has pleiotropy (MR-Egger: p-Egger intercept <0.05, Table 2), the relationship still does not exist after performing CAUSE analysis (p = 0.94). There is no evidence of heterogeneity according to the findings of Cochran’s Q test and the funnel plot (Fig. 3, Table 2). Additionally, the robustness of the MR estimates is verified by the leave-one-out analysis (Fig. 4). Figure 5 demonstrates the casual estimate of each SNP on dementia due to Parkinson’s disease.

Table 6.

The opinion about the relationship between MetS, its components and dementia in references

Author Study Relationship Opinion
Ng TP [6] Singapore Longitudinal Ageing Study Cohort MetS and any dementia Harm
Akbaraly TN [7] Whitehall II study MetS and any dementia Harm
Muller M [8] Multiethnic elderly cohort MetS and any dementia None
Atti AR [27] Meta-Analysis of Longitudinal Studies MetS and any dementia None
Watts AS [9] – MetS and any dementia Protective
Abbatecola AM [28] – WC and any dementia Harm
Ong HL [29] Cross-sectional epidemiological study WC and any dementia None
Walker KA [31] – Hypertension and any dementia Harm
Sierra C [30] – Hypertension and any dementia Unknown
Jennings JR [32] – Hypertension and any dementia Harm
Barbiellini Amidei C [33] and Mortimer JA [34] – FBG and any dementia Harm
Reitz C, Li J, Han KT [35–37] – TG, HDL-C and any dementia Inclusive
Atti AR [27] Meta-analysis MetS and AD None
Lee JE [39] – MetS and AD Harm
Forti P [40] Prospective population-based cohort MetS and AD Protective
Danat IM [41] Meta-analysis WC and AD None
Singh-Manoux A [42] Whitehall II Study WC and AD Harm
Raffaitin C and Solfrizzi V [43, 44] – MetS and vascular dementia Harm
Golimstok A [46] Case-control study FBG and frontotemporal dementia Harm
Schelp AO [50] Cross-sectional study MetS, its components and dementia due to Parkinson’s disease None
Dou Y, Yasuno F, Svensson T [51–53] – HDL-C and dementia with Lewy bodies Protective
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AD, Alzheimer’s disease; MetS, metabolic syndrome; WC, waist circumference; FBG, fasting blood glucose; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol.

DISCUSSION

In our MR analysis, we find that no significant causal association exists between MetS, its five components, and different dementia types, including any dementia, AD, vascular dementia, frontotemporal dementia, dementia with Lewy bodies, and dementia due to Parkinson’s disease, except for the relationship between HDL-C and dementia with Lewy bodies. HDL-C may play a protective role in dementia with Lewy bodies.

The previous results of the association between MetS, its components, and dementia is summarized in Table 6. The role of MetS on any dementia is not yet concluded. Some studies support the association between MetS and any dementia. For example, a cohort study including 1,519 participants conducted in Singapore finds that the MetS is associated with an increased risk of dementia [6]. The findings in the Whitehall II study also reveal that persistent MetS decline cognitive performance in late midlife [7]. In contrast, other studies do not support the association. In a cross-sectional and prospective study consisting of 2,476 men and women aged 65 years, researchers find that MetS is not associated with the increasing risk of dementia after 4.4 years of follow-up [8]. A recent meta-analysis including 18,313 participants ranging from January 1, 2000 to August 31, 2018 shows that no statistical significance pooled association emerges between MetS and dementia [27]. Some studies even support the protective role of MetS on dementia [9]. In our MR study, we do not identify the causal association between MetS and any dementia.

For the relationship between five components of MetS and any dementia, the association remains inconsistent. As to waist circumference, Abbatecola and his colleagues think that WC can predict the risk of cognitive decline during the 12-year follow-up in older patients with diabetes [28]. However, a study including 2,565 men and women does not find the association [29]. In our MR study, we do not support the causal association. The effect of hypertension on dementia remains unclear. Considering the numerous factors affecting hypertension, such as age and hypertension chronicity, the role of hypertension in dementia is complex [30]. For example, large epidemiological studies have demonstrated a consistent association between high midlife blood pressure and cognitive decline, while a similar association between late-life blood pressure and cognition decline is not consistent [31]. From the perspective of neuroimage, a recent study finds that hypertension may alter brain structure and function, which may result in disruption in cognitive function [32]. However, the causal association between hypertension and dementia does not exist in this study. FBG represents the abnormality of glucose level and is recognized as a well-known risk factor for dementia [33, 34], while we do not identify the causal association. In the association of TG, HDL-C, and dementia, the results also remain inclusive [35–37]. Our MR analysis does not find a causal relationship.

Inconsistent conclusions are also obtained about the association between MetS, its components, and AD [27, 38]. A meta-analysis, including a total of 18,313 participants aged older than 40 years with mean MetS prevalence of 22.7% and followed on average for 9.41 years, found that no significant pooled association existed between MetS and AD [27]. However, contradictory results also been reported [39], and the inverse association also have been observed [40]. As for MetS components, the effects on AD remain inconsistent. For example, a meta-analysis including 16 cohort studies and 41,781 participants and 4,511 dementia cases, no beneficial impacts of obesity in older age on incident dementia is found [41]. However, a study including a total of 10,308 adults found the detrimental effects on AD incidence [42]. In our MR study, no causal association between MetS, its components and the risk of AD were identified.

The studies related to the role of MetS on vascular dementia support the detrimental effect of MetS and may increase the risk of vascular dementia [43, 44], although these studies are scarce. In the Italian Longitudinal Study on Ageing including a total of 2,097 participants (MetS subjects [n = 918], subjects without MetS [n = 1,179]), studies found that MetS elevated the risk of vascular dementia [44]. So far, potential associations between frontotemporal dementia, and head trauma [45], diabetes [46], and autoimmune conditions may exist [47]. However, the study about the causal association between MetS and frontotemporal dementia is limited [48]. The study related to the association between MetS and dementia with Lewy bodies [49] and dementia due to Parkinson’s disease is also scarce, and no association between MetS, its components and dementia due to Parkinson’s disease was identified [50]. In our MR study, we find no significant casual association between MetS, its components and vascular dementia, frontotemporal dementia, and dementia due to Parkinson’s disease. As for dementia with Lewy bodies, Dou and colleagues thought that reduced levels of HDL-C were associated with the development of dementia with Lewy bodies in a case-control study including 65 patients with Lewy body dementia and 110 older adult controls [51]. Several studies also supported the relationship [52, 53].

Many observational studies may be influenced by many confounding factors such as limited sample size or (and) retrospective study. The strength of our MR study overcomes the possible confounders and clarifies the causal association between MetS and different dementia types. Additionally, it is the first study to illustrate their association. However, this study has several limitations. Firstly, the cases of different dementia are relatively small. Second, there is an ethnic bias because the datasets are all of European ancestry, which may limit the generalization of the conclusion. Third, we do not make stratification based on some factors such as age and gender due to the unavailability of stratification datasets. Future studies are required to verify these association in other ancestries, larger studies, and proper stratification people.

Conclusion

In our MR study, MetS and its components do not increase the risk of different dementia types., while HDL-C may play a protective role in dementia with Lewy bodies.

ACKNOWLEDGMENTS

We give great appreciation to the participants and working staff for their excellent job to the study.

FUNDING

This study was supported by the 1 3 5 project for disciplines of Excellence-Clinical Research Incubation Project, West China Hospital, Sichuan University (2018HXFH010).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

All data in our MR analyses are available from public databases (https://gwas.mrcieu.ac.uk/).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data in our MR analyses are available from public databases (https://gwas.mrcieu.ac.uk/).

Articles from Journal of Alzheimer's Disease are provided here courtesy of IOS Press

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