Thyroid-stimulating hormone and the risk of Alzheimer's disease: an ADNI cohort study

doi:10.21203/rs.3.rs-2114827/v1

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Thyroid-stimulating hormone and the risk of Alzheimer's disease: an ADNI cohort study

https://doi.org/10.21203/rs.3.rs-2114827/v1

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Background:

The association of thyroid function with Alzheimer's disease (AD) is controversial. This study mainly aimed to investigate the association between thyroid-stimulating hormone (TSH) and the risk of AD.

Methods:

We investigated the cross-sectional association between TSH and cognition, cerebrospinal fluid (CSF) biomarkers, and neuroimaging by linear regression models. The association between TSH and the risk of MCI conversion to AD within four years was measured by Cox proportional hazards models. Additionally, we investigated the interaction effects between TSH and sex in analyses.

Results:

A total of 476 participants who measured plasma TSH at baseline were included in the analyses, comprising 49 cognitively normal (CN), 336 mild cognitive impairment (MCI), and 91 AD. Within four years of follow-up, 160 MCI participants converted to AD. No associations were found between TSH and cognition and AD biomarkers. There existed sex differences in the association between TSH within the normal range and the risk of AD (p for interaction = 0.043). The highest tertile of TSH within the normal range significantly increased the risk of AD in female compared to the lowest tertile (HR = 2.62, p = 0.021). TSH was not associated with the risk of AD in male.

Conclusions:

High plasma levels of TSH within the normal range were associated with an increased risk of AD in female but not in male.

Alzheimer's disease

cognition

pathology

thyroid function

thyroid-stimulating hormone

risk factors

Thyroid dysfunction is generally believed to cause cognitive impairment. Thus, many guidelines recommend screening thyroid function for the initial evaluation of dementia[1–3], the most used marker that reflects thyroid function is plasma thyroid-stimulating hormone (TSH). Alzheimer's disease is the most common type of dementia and is estimated to affect 416 million persons across the world [4]. However, the association between TSH and the risk of AD is conflicting[5–12]. In addition, Alzheimer’s pathological changes have occurred 10–20 years before the onset of cognitive impairment[13, 14], but few studies have comprehensively investigated the association between TSH and AD biomarkers. Thus, the main purpose of this study was to evaluate the association between TSH and the risk of MCI conversion to AD. The second purpose was to further investigate the association between TSH and cognitive function and Alzheimer's pathology reflected by CSF and neuroimaging biomarkers.

Participants

We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI is a North American cohort study launched in 2003. The major goals of ADNI are to develop and test biological and imaging biomarkers for the early detection of mild cognitive impairment (MCI) and AD. Detailed inclusion and exclusion criteria of participants are present on the ADNI website. MCI participants were followed up at 6, 12, 18, 24, 36, and 48 months. MCI-converter was defined as MCI subjects that converted to AD within four years. All enrolled participants provided informed consent and the ADNI study was approved by the local ethics committee.

TSH measurements

Fasting plasma samples were collected in the morning. TSH was measured by Targeted Multiplex Proteomic on the Luminex xMAP platform (Luminex 100; Luminex Corporation)[15]. The Luminex xMAP technology uses fluorescent polystyrene microspheres to capture and detect the analytes, with high accuracy and sensitivity. The quality control (QC) samples included 3 concentrations: low, middle, and high. The coefficient of variation (CV) for TSH in each concentration was 10.6%, 8.26%, and 8.01%. CV values below 25% were acceptable according to the suggestion of the Biomarkers Consortium Project Team. The least detectable dose (LDD) of TSH was 0.021 mIU/L. We used 0.45 to 4.49 mIU/L as the reference range for normal TSH values according to the suggestion of Thyroid Studies Collaboration[16]. The data of TSH values were derived from Biomarkers Consortium Plasma Proteomics Project RBM multiplex data.

Assessment of cognition

Cognitive function was evaluated by the Alzheimer Disease Assessment Scale 13-item cognitive subscale (ADAS-Cog13; range, 0 [best] to 85 [worst] points) , the Mini-Mental State Examination (MMSE; range, 0 [worst] to 30 [best] points) and the sum of boxes of the Clinical Dementia Rating (CDR-SB; range, 0 [best] to 18 [worst] points).

Assessment of AD biomarkers

CSF biomarkers (Aβ42, t-tau, and p-tau181) were measured by Roche Elecsys electrochemiluminescence immunoassays. Hippocampal, ventricular, and whole-brain volumes were calculated to indicate brain atrophy by the magnetic resonance imaging (MRI) T1-weighted images. The glucose uptake of regions of interest (MetaROIs) was measured by [18F] fluorodeoxyglucose-positron emission tomography (FDG-PET). The average FDG-PET standardized uptake value ratios (SUVR) of angular, temporal, and posterior cingulate were intensity normalized using the pons/vermis as reference region. [11C]-PiB PET (Aβ-PET) was used to estimate the brain Aβ burden. The average PIB SUVR of the frontal cortex, anterior cingulate, precuneus cortex, and parietal cortex were calculated using the cerebellum as a reference region.

Assessment of covariates

APOE allele status was tested using DNA extracted from peripheral blood cells by quantitative polymerase chain reaction assays (TaqMan; Qiagen)[15]. Sitting blood pressure was measured in the same arm using the same calibrated mercury sphygmomanometer and blood pressure cuff with the participants keeping calm at baseline[17]. History of diabetes or hyperlipidemia was defined by the medical recording at baseline. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters at baseline. Depression scores were measured by Geriatric Depression Scale (15-item version) at baseline[18].

Statistical analyses

Shapiro-Wilk test was used to inspect the normality of all continuous data. Data are presented as the mean ± standard deviation for normally distributed values or median (IQR) for skewed distributed values for continuous variables and number (%) for categorical variables. Group differences were compared by one-way analysis of variance (ANOVA) for normally distributed variables, Kruskal Wallis non-parametric test for skewed distributed variables, and Pearson chi-square test for categorical variables.

The TSH was further investigated to assess the cross-sectional relationship between cognition and AD biomarkers by linear regression (LM) models. All LM models were always adjusted for age, sex, and APOE ε4. Education levels were added as an additional covariate for cognition, and intracranial volume (ICV) was added as an additional covariate for hippocampal, ventricular, and whole-brain volumes.

Cox proportional hazards regression model was used to evaluate the effects of TSH on AD risk. Participants were censored at their last visit. Three models were used in cox proportional hazards regression to adjust different covariates. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and APOE ε4. Model 3 was further adjusted for blood pressure, history of diabetes, history of hyperlipidemia, BMI, and depression.

Sex differences in the relationship between TSH and cognition, AD biomarkers, and the risk of AD were assessed by adding an interaction term between TSH and sex in the whole group regression model. If the interaction was significant, sex-stratified analyses would be conducted independently in male and female.

All statistical analyses were conducted using the SPSS 26.0 statistical software (SPSS Inc., Chicago, IL, USA). Two-sided p < 0.05 was considered statistically significant for all tests.

Demographics of participants

A total of 566 participants with plasma TSH measurement at baseline (Figure 1). One participant with plasma TSH values lower than the LDD was identified as an outlier and was excluded from analyses. And 89 participants who had the using record of thyroid medications, whether anti-thyroid medications or thyroid hormone replacement, at baseline or during follow-up were excluded from analyses also. Finally, 476 participants were included in this study comprising 49 cognitively normal (CN), 336 mild cognitive impairment (MCI), and 91 AD. Only three participants with baseline TSH values below the reference range, and 25 participants with baseline TSH values above the reference range. Since few participants completed the Aβ-PET scan at baseline, we used the Aβ-PET data obtained 12 months after baseline and TSH measurement at the same visit with Aβ-PET to investigate the cross-sectional association between TSH and Aβ-PET. All participants had covariates data available.

After 718.0 person-years of follow-up (median 2.0, IQR 2.0 years), 160 MCI converted to AD, and 156 MCI did not convert to AD (14 participants without one visit and 6 participants with MCI converted to other forms of dementia were excluded from analyses, Table 2). There were no differences in baseline TSH levels between participants stratified by baseline diagnosis or MCI conversion (Table 1-2).

Table 1 Baseline demographics of participants

	CN (n = 49)	MCI (n = 336)	AD ( n= 91)	P
Age	73.10 (7.60)	75.1 (9.78)	75.30 (11.25)	0.765
Sex:Female, No.(%)	22 (44.9)	104 (31.0)	36 (39.6)	0.073
Education (years)	16.00 (4.00)	16.00 (4.00)	16.00 (5.00)	0.238
≥1 APOE ε4, No.(%)	4 ( 8.2)	183 (54.5)	64 (70.3)	<0.001
ADAS-Cog13	10.00 (5.66)	18.33 (8.66)	28.67 (11.67)	<0.001
MMSE	29.00 (1.00)	27.00 (2.00)	24.00 (3.00)	<0.001
CDR-SB	0.00 (0.00)	1.50 (1.00)	4.00 (1.50)	<0.001
CSF Aβ42 (pg/mL)	1458.00 (408.75)	666.20 (464.75)	547.60 (299.35)	<0.001
CSF t-tau (pg/mL)	205.75 (76.30)	288.90 (147.10)	345.30 (169.00)	<0.001
CSF p-tau181 (pg/mL)	17.49 (6.45)	27.78 (17.66)	33.57 (17.25)	<0.001
Hippocampus (mL)	7.32 ± 0.83	6.42 ± 1.08	5.85 ± 1.15	<0.001
Ventricles (mL)	30.90 (20.68)	39.94 (30.20)	45.40 (32.67)	<0.001
Whole-brain (mL)	1002.98 ± 98.06	1003.49 ± 111.81	973.31 ± 124.51	0.078
FDG-PET	1.31 ± 0.11	1.19 ± 0.13	1.07 ± 0.14	<0.001
Aβ-PET^*	1.31 (0.11)	1.98 (0.72)	1.87 (0.38)	0.069
TSH (mIU/L)	2.10 (1.30)	1.80 (1.40)	1.90 (1.15)	0.495

*Measured at 12 months after baseline.

Table 2 Demographics of participants stratified by MCI conversion^*

	MCI-nonconverter (n = 156)	MCI-converter ( n= 160)	P
Age	74.85 (9.75)	75.80 (9.63)	0.913
Sex:Female, No.(%)	39 (25.0)	58 (36.2)	0.041
Education (years)	16.00 (6.00)	16.00 (4.00)	0.205
≥1 APOE ε4, No.(%)	74 (47.4)	102 (63.7)	0.005
BMI	26.12 (4.35)	25.15 (4.71)	0.022
SBP	135.50 (27.00)	137.00 (22.50)	0.422
DBP	75.17 ± 9.53	74.42 ± 9.68	0.489
Diabetes, No.(%)	13 ( 8.3)	12 ( 7.5)	0.947
Hyperlipidemia, No.(%)	62 (39.7)	75 (46.9)	0.244
Depression scores	1.00 (1.00)	1.00 (1.00)	0.946
TSH (mIU/L)	1.90 (1.50)	1.70 (1.43)	0.424

*14 participants without one visit and 6 participants with MCI converted to other forms of dementia were excluded from analyses. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Associations between TSH and cognition and Alzheimer’s pathology

As shown in Table 3, we observed no significant associations between TSH and cognition and AD biomarkers (all p > 0.05). There were also no significant interactions between sex and TSH on these measures (all p for interaction > 0.05).

Table 3 Cross-sectional associations between TSH and other measures

	n	B	95% CI	p	p for interaction
ADAS-Cog13	471	-0.28	-0.84, 0.28	0.333	0.165
MMSE	476	-0.07	-0.23, 0.09	0.41	0.530
CDR-SB	476	-0.02	-0.13, 0.09	0.731	0.143
CSF Aβ42	290	9.4	-24.78, 43.57	0.589	0.976
CSF t-tau	290	-3.7	-15.41, 8.03	0.536	0.304
CSF p-tau181	290	-0.58	-1.86, 0.71	0.378	0.224
Hippocampus	378	-0.01	-0.09, 0.07	0.739	0.179
Ventricles	465	-0.99	-2.32, 0.34	0.143	0.192
Whole-brain	468	1.05	-3.05, 5.16	0.615	0.555
FDG-PET	248	0.004	-0.01, 0.02	0.632	0.792
Aβ-PET*	61	-0.01	-0.10, 0.08	0.763	0.556

*Measured at 12 months after baseline.

Association between TSH and the risk of AD

We found no significant association or sex differences between TSH and the risk of conversion from MCI to AD in the full range of TSH (Table 4). However, we found that sex modified the TSH-associated risk of AD in the normal range of TSH (p for interaction = 0.043, model 3, Table 4). Sex-stratified analyses in the normal range of TSH seemed to indicate an increased risk of AD in male with higher levels of TSH (HR = 1.33, p = 0.077, model 3, Table 5). The association was not significant, which may be due to the smaller sample sizes. In analyses stratified TSH into tertiles, we found the highest TSH tertile significantly increased the risk of AD in female compared with the lowest TSH tertile within the normal range (HR = 2.62, p = 0.021, model 3, Table 5). TSH was not associated with AD risk within the full or normal range of TSH in male.

Table 4 Association between TSH and the risk of AD*

	n	Model 1				Model 2				Model 3
	n	HR	95%CI	P	P for interaction	HR	95%CI	P	P for interaction	HR	95%CI	P	P for interaction
TSH	316	1.00	0.89, 1.12	0.980	0.185	0.98	0.87, 1.10	0.725	0.205	0.98	0.87, 1.11	0.793	0.173
TSH within the normal range (0.45-4.49 mIU/l)	296	0.99	0.83,1.18	0.874	0.025	0.97	0.82，1.16	0.759	0.033	0.97	0.81,1.15	0.696	0.043

*Model 1: unadjusted. Model 2: adjusted for age, sex, and APOE ε4. Model 3: further adjusted for blood pressure, diabetes, hyperlipidemia, BMI, and depression scores.

Table 5 Association between TSH and AD risk within the normal range stratified by sex*

	n	Model 1			Model 2			Model 3
	n	HR	95%CI	P	HR	95%CI	P	HR	95%CI	P
Continuous TSH
Male	209	0.84	0.67,1.07	0.159	0.83	0.66,1.05	0.123	0.84	0.67,1.06	0.149
Female	87	1.28	0.97,1.69	0.076	1.26	0.95,1.66	0.108	1.33	0.97,1.83	0.077
Tertiles of TSH
Male
Lowest (0.45-1.40mIU/L)	77	Reference
Middle (1.50-2.10mIU/L)	64	1.05	0.67,1.66	0.824	1.06	0.67,1.67	0.81	1.10	0.69,1.77	0.688
Highest (2.20-4.30mIU/L)	68	0.68	0.41,1.14	0.142	0.67	0.40,1.12	0.123	0.71	0.42,1.20	0.199
Female
Lowest (0.46-1.30mIU/L)	31	Reference
Middle (1.40-2.30mIU/L)	28	1.76	0.89.3.47	0.104	1.63	0.82,3.26	0.164	2.26	1.03,4.96	0.042
Highest (2.40-4.20mIU/L)	28	1.93	0.98,3.79	0.058	1.91	0.97,3.77	0.062	2.62	1.16,5.94	0.021

*Model 1: unadjusted. Model 2: adjusted for age, sex, and APOE ε4. Model 3: further adjusted for blood pressure, diabetes, hyperlipidemia, BMI, and depression scores.

Sensitive analyses

Stratified by baseline diagnosis analyses still suggested no correlations between TSH with cognition and AD biomarkers in each group (Additional file, Table 1-3). There were also no correlations between the above measures and TSH within the normal range (Additional file, Table 4).

The main findings of this study were as follows. First, TSH was not associated with cognitive function and AD pathology. Second, we found there exited sex differences in the association between TSH levels and the risk of AD when analyses limited to TSH within the normal range. Female MCI participants who had TSH levels in the highest tertile had a more than twofold increased risk of AD compared with those in the lowest tertile. TSH was not associated with the risk of AD in male.

Previous eight prospective studies about the association between TSH and AD risk were conflicting[5–12]. Five studies found no significant association[5, 7–9, 12], while two studies reported low TSH levels were associated with an increased risk of AD[6, 11]. Only one study investigated sex differences in TSH-associated risk of AD, and they found whether low or high levels of TSH would increase the risk of AD in female, but TSH was also not associated with AD risk in male[10]. Important differences between these studies and ours include the cognitive function and thyroid function of participants at baseline, which may explain the diversity of results. The baseline diagnosis of participants in these studies included nondemented, cognitively normal, and MCI. Follow-up of people with diverse baseline cognitive statuses may lead to different results to conversion. Since the use of thyroid medication can affect the TSH levels, it is better to control the influence of thyroid medication. But most studies failed to exclude the influence of thyroid medication or just exclude participants using thyroid medication at baseline in the analyses. A strength of our study is that thyroid medication users were excluded at any time point of the follow-up, which is based on the detailed medical recording in ADNI. Moon et al.[6] also investigated the risk of MCI conversion to AD within the normal range of TSH, they found that low TSH levels were associated with the risk of AD, but only 8 MCI participants converted to AD in their study. In addition, none of the above studies investigated the association between TSH and AD biomarkers.

Several basic studies have proposed possible mechanisms to explain the association between thyroid hormones and AD. Triiodothyronine (T3) and tetraiodothyronine (T4) have been shown to inhibit the gene expression of amyloid-beta precursor protein (APP) in mouse brain[19, 20], these findings suggested that low thyroid hormone levels may directly contribute to the pathogenesis of AD by increasing the deposition of amyloid protein in the brain. Besides, several population-based studies have found that high plasma TSH levels were associated with AD pathological changes, including increased brain amyloid burdern[21], decreased brain glucose metabolism[22], and changed CSF biomarkers[23]. However, no associations were found between TSH and AD pathology in our study. Low thyroid hormones have been known to result in the compensatory elevation of TSH. Whether the development of AD dues to high TSH levels or is the consequence of reduced thyroid hormones, the specific mechanism is unknown.

In our study, we also observed that sex difference in the association between TSH within the normal range and AD risk. The explanation for the sex difference in TSH-associated risk of AD is unclear. But many studies proposed the potential mechanisms of sex-specific contributions to AD, including the different impacts of sex hormones, APOE genotype, inflammation, metabolism, and comorbidities in AD pathogenesis[24, 25]. Sex differences in AD pathophysiology highlight the importance of sex-specific prevention measures and treatments for AD in clinical practices.

Besides the confounding induced by thyroid medication was excluded, the strengths of this study also include the rigorous inclusion/exclusion criteria for MCI. The subtype of MCI in ADNI is amnestic MCI measured by education-adjusted scores on logical memory and excludes the depressed or vascular etiology, which is most likely derived from AD. The inclusion of other types of MCI may underestimate the risk of converting to AD. Furthermore, we systematically investigated the association between TSH and cognition and AD biomarkers.

However, this study exists several limitations. First, our study lacked the measurements of T3 and T4, so the association between these thyroid hormones and AD could not be investigated. Second, due to the small sample sizes of participants with TSH levels above the normal range in our study, we were not able to investigate the association between TSH levels above the normal range and the risk of AD. Third, all participants in ADNI come from North America, which limits the external validity of the results and our findings need to be validated in other regions. Last, the participants measured Aβ-PET in our study was limited, the cross-sectional association between TSH and amyloid deposition in the brain needs to be validated in larger-scale studies.

In summary, high levels of TSH within the normal range were associated with an increased risk of AD in female but not in male. Given the heterogeneity of existing evidences, further high-quality prospective studies are still needed. We recommend future studies to clearly define the baseline cognitive status and thyroid function of participants included in analyses, to enhance the comparability of similar studies.

CN: cognitively normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; BMI: body mass index; ADAS-Cog13: Alzheimer Disease Assessment Scale 13-item cognitive subscale; MMSE: Mini-Mental State Examination; CDR-SB: Clinical Dementia Rating Scale Sum of Boxes; CSF: cerebrospinal fluid; MRI: magnetic resonance imaging; FDG-PET: fluorodeoxyglucose–positron emission tomography; QC: quality control; CV: coefficient of variation; LDD: least detectable dose; LM: linear regression; ICV: intracranial volume; SUVR: standardized uptake value ratios; TSH: thyroid-stimulating hormone; ADNI: Alzheimer’s Disease Neuroimaging Initiative.

Ethics approval and consent to participate

The ADNI study was reviewed and approved by the local ethics committee. The participants provided their written informed consent to participate in this study.

Consent for publication

Not applicable.

Availability of data and materials

Data used in the preparation of this article were obtained from the ADNI database,

http://www.adni-info.org.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 81974220) and Central health research project (Grant No. 2020ZD10).

Author contributions

BZ conceptualized the study, analyzed the data, and drafted the manuscript. CZ and YTX analyzed the data. YYW and LAC revised the manuscript. YNQ and DTP conceptualized the study and revised the manuscript. All authors reviewed and approved the final version of the manuscript.

Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica, Inc., Biogen, BristolMyers Squibb Company, CereSpir, Inc., Cogstate; Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company, EuroImmun, F.Hoffmann-La Roche Ltd., and its affiliated company Genentech, Inc., Fujirebio, GE Healthcare, IXICO Ltd., Janssen Alzheimer Immunotherapy Research and Development, LLC., Johnson & Johnson Pharmaceutical Research and Development, LLC., Lumosity, Lundbeck; Merck & Co., Inc., Meso Scale Diagnostics, LLC., NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc., Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California.

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No competing interests reported.

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Thyroid-stimulating hormone and the risk of Alzheimer's disease: an ADNI cohort study

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