The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis

Yiliang Liu; Xuewen Xiao; Yang Yang; Rui Yao; Qijie Yang; Yuan Zhu; Xuan Yang; Sizhe Zhang; Lu Shen; Bin Jiao

doi:10.1002/alz.14049

. 2024 Jun 11;20(7):4841–4853. doi: 10.1002/alz.14049

The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis

Yiliang Liu ¹, Xuewen Xiao ¹, Yang Yang ^1,^2,^3,^4,⁵, Rui Yao ¹, Qijie Yang ¹, Yuan Zhu ¹, Xuan Yang ¹, Sizhe Zhang ¹, Lu Shen ^1,^2,^3,^4,⁵, Bin Jiao ^1,^2,^3,^4,^5,^✉

PMCID: PMC11247675 PMID: 38860751

Abstract

INTRODUCTION

The cognitive impairment patterns and the association with Alzheimer's disease (AD) in mental disorders remain poorly understood.

METHODS

We analyzed data from 486,297 UK Biobank participants, categorizing them by mental disorder history to identify the risk of AD and the cognitive impairment characteristics. Causation was further assessed using Mendelian randomization (MR).

RESULTS

AD risk was higher in individuals with bipolar disorder (BD; hazard ratio [HR] = 2.37, P < 0.01) and major depressive disorder (MDD; HR = 1.63, P < 0.001). MR confirmed a causal link between BD and AD (OR_IVW= 1.098), as well as obsessive‐compulsive disorder (OCD) and AD (OR_IVW= 1.050). Cognitive impairments varied, with BD and schizophrenia showing widespread deficits, and OCD affecting complex task performance.

DISCUSSION

Observational study and MR provide consistent evidence that mental disorders are independent risk factors for AD. Mental disorders exhibit distinct cognitive impairment prior to dementia, indicating the potential different mechanisms in AD pathogenesis. Early detection of these impairments in mental disorders is crucial for AD prevention.

Highlights

This is the most comprehensive study that investigates the risk and causal relationships between a history of mental disorders and the development of Alzheimer's disease (AD), alongside exploring the cognitive impairment characteristics associated with different mental disorders.
Individuals with bipolar disorder (BD) exhibited the highest risk of developing AD (hazard ratio [HR] = 2.37, P < 0.01), followed by those with major depressive disorder (MDD; HR = 1.63, P < 0.001). Individuals with schizophrenia (SCZ) showed a borderline higher risk of AD (HR = 2.36, P = 0.056).
Two‐sample Mendelian randomization (MR) confirmed a causal association between BD and AD (OR_IVW= 1.098, P < 0.05), as well as AD family history (proxy‐AD, OR_IVW= 1.098, P < 0.001), and kept significant after false discovery rate correction. MR also identified a nominal significant causal relationship between the obsessive‐compulsive disorder (OCD) spectrum and AD (OR_IVW= 1.050, P < 0.05).
Individuals with SCZ, BD, and MDD exhibited impairments in multiple cognitive domains with distinct patterns, whereas those with OCD showed only slight declines in complex tasks.

Keywords: Alzheimer's disease, bipolar disorder, cognitive impairment, Mendelian randomization, mental disorder, obsessive‐compulsive disorder, schizophrenia

1. BACKGROUND

Alzheimer's disease (AD) is a prevalent neurodegenerative disease characterized by progressive, irreversible cognitive impairment, imposing a significant burden on patients and health‐care systems globally. ¹ The International Classification of Diseases 11th Revision (ICD‐11) categorizes mental disorders as syndromes that impair cognition, emotional regulation, or behavior, significantly disrupting individuals' daily lives. ²

There is a notable symptomatic overlap between AD and various mental disorders. Severe mental conditions, such as schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), can lead to cognitive impairments in diverse domains, including attention, social cognition, processing speed, working memory, verbal learning, and visual learning. ³ , ⁴ , ⁵ Conversely, many AD patients also experience psychosis, particularly in the advanced stages of the disease. ⁶ This symptom overlap may be partially attributable to genetic links between certain mental disorders and AD. Recent studies have identified specific genetic loci shared by AD and MDD, with emerging evidence suggesting that MDD might play an important role in AD development. ⁷ , ⁸ Moreover, genetic factors associated with SCZ have been implicated in the development of psychosis in AD patients. ⁹

Multiple epidemiological studies have identified depression as a potential independent risk factor for cognitive decline and AD. ¹⁰ , ¹¹ , ¹² A review of meta‐analyses estimated the risk of AD in individuals with a history of depression, presenting an odds ratio (OR) of 1.54 (P = 0.038). ¹³ Researchers have also suggested that anxiety disorder (ANX) significantly increases the risk of AD with a hazard ratio (HR) of 3.16 (P < 0.001) and an OR of 4.389 (P < 0.05), even after adjustments for variables including sex, age, education, race, apolipoprotein E (APOE) ε4 status, and hypertension. ¹⁴ , ¹⁵

However, traditional epidemiological studies often face challenges due to potential unseen biases. In 2017, Tapiainen et al. highlighted the association between mental and behavioral disorders and an increased risk of AD within a 5‐year time window, but these associations diminished over a 10‐year period. ¹⁶ Conversely, Richmond‐Rakerd et al. in 2022 reported a 30‐year longitudinal study showing that individuals with mental disorders developed dementia ≈ 5.60 years earlier than those without. ¹⁷ An umbrella review encompassing 10 systematic reviews suggested that while high‐to‐moderate evidence supports the associations between certain mental disorders (BD, MDD, SCZ, etc.) and dementia, nine of these reviews were found to be at high risk of bias. ¹⁸ Mendelian randomization (MR) analyses, a newer tool for causal inference, can mitigate the issues of confounding and reverse causation often present in observational studies, as genetic variants are inherited randomly across generations. ¹⁹ In 2022, Wei et al. conducted a pilot bidirectional MR analysis exploring the association between mental disorders and AD, covering five major psychiatric disorders, including SCZ, BD, and ANX. ²⁰ However, as genome‐wide association studies (GWAS) for AD and other mental disorders have been extensively updated since this research, ²¹ , ²² , ²³ further investigation is needed into the wider range of mental disorder phenotypes and the unbiased association between AD and these disorders.

In this study, we hypothesized that a history of mental disorders increases the risk of AD and that some of these associations may be causal. Using data from the UK Biobank (UKB), we examined the AD risk among individuals with a history of mental disorders and used bidirectional two‐sample MR to establish causality. Furthermore, we assessed the cognitive performance of participants at baseline to identify patterns of cognitive impairment associated with mental disorders, and further discussed potential mechanisms that underlie the shared pathogenesis between mental disorders and AD.

2. METHODS

2.1. Participants

The UKB is a prospective cohort study that has amassed comprehensive genetic, physical, and health data from > 500,000 participants. These individuals, aged between 40 and 69, resided in the United Kingdom and were enrolled from 2006 to 2010. After their initial assessment and consent, a subset of participants was subsequently invited to undergo repeated assessments in various batches from 2012 to the present. Health‐related outcomes have been automatically recorded and updated through linked data from hospital admissions, which include diagnoses and procedures, as well as death registries. ²⁴

Out of the 502,250 participants in the UKB up to January 5, 2024, we excluded 15,953 individuals based on the following criteria: (1) loss to follow‐up; (2) a baseline diagnosis of neurological diseases that directly affect cognition, including Parkinson's disease, dementia, stroke, head injury/trauma, epilepsy, cerebral palsy, and encephalitis.

2.2. Mental disorders and dementia outcome in UKB

Exposure to a history of mental disorders was defined as having a diagnosis of any mental disorder at baseline, according to the self‐reported non‐cancer illness codes in UKB. Based on the mental disorders categories in UKB, exposed participants were classified into the following groups: SCZ, BD, MDD, ANX, eating disorder (ED), obsessive‐compulsive disorder (OCD), post‐traumatic stress disorder (PTSD), substance use disorder (SUD), and two or more mental disorder comorbidities (CMB). ²⁵ These data were obtained through participant‐completed questionnaires under the supervision of trained medical personnel, supplemented by the integration of electronic health records (EHR) from multiple health‐care systems across the UK. ²⁶

The primary outcome was the clinical diagnosis of AD, as identified in EHR‐linked illness records. These records included ICD‐10 codes assigned by clinical doctors based on hospital admissions and death registries, and in this study, with a cutoff date of January 2024. ²⁷ Additionally, other types of dementia documented in the UKB, such as all‐cause dementia, vascular dementia (VD), and frontotemporal dementia (FTD), were also recorded.

Research in context

Systematic review: The authors conducted a thorough review of the literature on the association between mental disorders and dementia. Many existing epidemiological studies faced challenges such as potential biases, limitations in sample size, and the diversity of mental disorders examined. Moreover, there has been a lack of comprehensive research focusing on the patterns of cognitive impairment across various mental disorders.
Interpretation: This research provides evidence supporting the hypothesis that different mental disorders are characterized by distinct cognitive impairments and may increase the risk of Alzheimer's disease (AD), with certain associations proving to be causal. Specifically, the consistent associations identified between bipolar disorder and AD, through epidemiological studies and Mendelian randomization analyses, suggest there may be shared genetic pathways or mechanisms with AD. The detailed examination of cognitive impairment patterns further revealed unique profiles among different mental disorders, indicating a potential commonality in the neurophysiological mechanisms underlying certain mental disorders and neurodegenerative processes.
Future directions: Theoretically, our findings suggest a potential shared mechanism between certain mental disorders and neurodegenerative processes, warranting further investigation to elucidate how these etiological pathways may intersect with AD pathogenesis. Clinically, the identification of these nuanced differences in cognition through more complex tasks provides an in‐depth understanding of the cognitive deficits unique to each disorder. This highlights the urgent need for tailored interventions aimed at optimizing patient care and outcomes.

2.3. Bidirectional two‐sample MR

To ascertain whether the observed association between mental disorders and AD in the UKB was causal, bidirectional two‐sample MR analyses were conducted following the Strengthening the Reporting of Observational Studies in Epidemiology‐Mendelian Randomization guideline (Table S1 in supporting information). These analyses aimed to assess the elevated risk of AD while mitigating biases due to confounding factors and reverse causality. For each mental disorder, summary‐level GWAS statistics were gathered from the most recent large‐scale studies in European ancestry. These population‐based cohort GWAS included SCZ (N = 320,404), ²⁸ BD (N = 413,466), ²⁹ MDD (N = 807,553), ³⁰ ED (N = 72,517), ³¹ OCD (N = 9725), ³² Tourette syndrome (TS) in OCD spectrum disease (N = 14,307), ³³ ANX (N = 21,761), ³⁴ PTSD (N = 106,655), ³⁵ SUD with alcohol (N = 141,932) ³⁶ and opioids (N = 41,176). ³⁷

Primary forward MR analyses used summary‐level GWAS statistics from the study by Kunkle et al., encompassing 21,982 AD cases and 41,944 controls. ²³ Additional validation was conducted using data from another extensive GWAS by Bellenguez et al., which included 111,326 individuals with clinically diagnosed AD or a family history of AD (proxy‐AD), alongside 677,663 controls, pooled from the European Alzheimer's Disease DNA BioBank (EADB) and UKB. ²¹ Reverse MR using AD/proxy‐AD as exposures and mental disorders as outcomes were also conducted. For detailed information about the sample sources from GWAS for each mental disorder and AD outcome see Table S2 in supporting information.

Each instrumental variable (IV) was selected based on stringent criteria to align with the foundational hypothesis of causal inference: (1) single nucleotide polymorphisms (SNPs) should be strongly associated with the exposure(s) of interest; (2) SNPs should be independent of any confounding factors; (3) SNPs should affect the outcome only through the exposure(s) of interest. ³⁸ SNPs meeting a genome‐wide significance threshold (P < 5 × 10^‐8) were considered potential IVs. For disorders such as ANX, OCD, TS, PTSD, and SUD with opioids, this threshold was adjusted to a more flexible level (P < 1 × 10^‐5) to ensure an adequate number of SNPs for analysis.

Furthermore, SNPs were selected based on several criteria: (1) a minor allele frequency (MAF) > 0.01; (2) minimal likelihood of linkage disequilibrium (r ²> 0.001, distance = 10,000 kb); (3) sufficient IV strength, as evaluated the by F statistic > 10 using the following formula: $F = (\frac{N - K - 1}{K}) \times (\frac{R^{2}}{1 - R^{2}})$ ³⁹ ; (4) lack of strong association with the outcome (P > 0.01); (5) avoidance of reverse causal effects, assessed using MR Steiger filtering (with default threshold setting of ‘mr_steiger’ function); and (6) not associated with other confounding phenotypes (searched GWAS catalog, with threshold of P < 1 × 10^‐5). SNPs not present in the outcome data were substituted with available proxy SNPs (r ²> 0.8), identified through LDlink. ⁴⁰ The ORs between these eight categories of mental disorders and AD were analyzed using two‐sample MR, aiming for unbiased results. ⁴¹

2.4. Cognitive impairment characteristic

The cognitive impairment patterns at baseline were further explored in different mental disorders. The cognitive test in UKB was reported to be valid while possessing a wide coverage of multifarious aspects of cognitive function. ⁴² Five categories of cognitive tests were selected including fluid intelligence (FI), pairs matching (PM), digits span (DS), symbol digit substitution (SDS), and Trail Making Test (TMT).

The FI test comprises 13 logical questions and each correct answer accounts for one score in FI. ⁴³ In the PM test, participants were told to match 3/6 pairs of cards and make as few mistakes as they could; the error times were recorded across the task. ⁴⁴ DS required participants to iterate the number of digits they had just heard until they made any mistake or reached the maximal 12 digits. ⁴⁵ In the SDS test, each number of digits matched with a certain symbol was presented to the participants. After a period of memorizing, the participants were asked to substitute every digit with the symbol they had just learned. ⁴⁶ TMT comprises numeric and alpha‐numeric tasks, and the participants have to draw a line to pass all of the points abiding by the numeral or alphabetic sequence. ⁴⁷

2.5. Statistical analysis

The attack rate (AR) of AD was defined as the proportion of participants who developed AD during the follow‐up period. The comparisons of OR between the control group and the exposure groups were performed using Fisher exact test for assessment. Multiple imputation was used to impute missing covariates in the statistical analyses. HR was obtained through multivariate Cox models after adjusting for potential confounding factors. The Wilcoxon rank sum test was conducted in the comparison of the mean age‐at‐onset (AAO) of AD and cognitive test performance.

In the MR analysis, the effect size was represented by β (i.e., the natural logarithm of the OR), indicating the association of each SNP with the specific phenotype. The Wald ratio was calculated by dividing the β associated with the outcome by the β associated with the exposure. When multiple SNPs were available, the inverse variance weighting (IVW) method was used as the preferred approach for meta‐analysis. This was contingent on confirming the absence of horizontal pleiotropy and heterogeneity. ⁴⁸ For multiple comparisons, the false discovery rate (FDR) correction was used to adjust the P values. Sensitivity analyses included methods such as MR Egger regression, ⁴⁹ weighted median, ⁵⁰ weighted modes, ⁵¹ simple modes, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR‐PRESSO). ⁵² The MR Egger intercept and Cochran Q statistics were applied to assess heterogeneity and pleiotropy. Power analysis was conducted to further validate the stability of the MR results. ⁵³ An additional sensitivity analysis was conducted to address overfitting bias caused by sample overlap among ED, PTSD, and proxy‐AD. ⁵⁴ All statistical analyses were conducted via R (v4.2.3) using “Survival” (v3.5‐7), “mice” (v3.16.0), “TwoSampleMR” (v0.5.6), “MRlap” (v0.0.3), and “MR PRESSO” (v1.0) packages.

3. RESULTS

3.1. Demographic characteristics of participants

The procedure of the observational study and specific settings in MR are presented in Figure 1. From the UKB database, 486,297 participants were selected based on the inclusion and exclusion criteria.

Flow chart of the research process. AD, Alzheimer's disease; ANX, anxiety disorder; BD, bipolar disorder; CMB, comorbidity of mental disorders; ED, eating disorder; MAF, minor allele frequency; MDD, major depressive disorder; MR, Mendelian randomization; OCD, obsessive‐compulsive disorder; PTSD, post‐traumatic stress disorder; QC, quality control; SCZ, schizophrenia; SNP, single nucleotide polymorphism; SUD, substance use disorder; UKB, UK Biobank

The participant breakdown is as follows: 404 with a history of SCZ, 1076 with BD, 25,936 with MDD, 5551 with ANX, 200 with ED, 60 with OCD, 260 with PTSD, 395 with SUD, and 5682 with CMB. Additionally, 446,733 participants without a reported history of mental disorders were categorized as the non‐condition (NC) control group. Table 1 presents participants' basic information, including age at recruitment, age at mental disorder diagnosis, disease duration up to the baseline assessment, and other demographic characteristics.

TABLE 1.

Characteristics of the participants from UKB (N = 486,297).

Demographics	Groups
Demographics	NC	SCZ	BD	MDD	ANX	ED	OCD	PTSD	SUD	CMB
Sample size (N)	446,733	404	1076	25,936	5551	200	60	260	395	5682
Age at recruitment (mean, SD)	56.6 (8.1)	54.4 (8.2)	55.6 (8.0)	55.4 (7.8)	55.6 (7.9)	52.0 (7.4)	54.8 (8.4)	55.1 (8.2)	55.7 (7.6)	53.4 (7.6)
Sex (female, %)	53.9	30.9	56.0	66.3	66.0	95.5	51.7	50.4	24.1	62.9
BMI (mean, SD)	27.3 (4.7)	28.5 (5.7)	28.7 (5.7)	28.4 (5.6)	27.1 (4.8)	22.2 (4.2)	27.6 (5.1)	28.8 (5.2)	26.9 (4.7)	27.7 (5.4)
Educational level (median, IQR)^a	2(1)	2(1)	2(1)	2(1)	2(1)	1(1)	2(2)	2(1)	2(1)	2(1)
Diabetes (%)	4.6	7.4	6.5	5.9	3.4	0.5	5.0	7.7	6.1	3.4
APOE ε4 carrier (%)	30.5	30.4	28.9	30.8	31.8	27.5	23.3	31.2	30.4	30.9
Age at mental disorder diagnosis (mean, SD)^b	–	32.1 (9.9)	38.0 (12.4)	43.6 (13.2)	45.7 (14.2)	22.5 (10.4)	36.0 (16.0)	46.9 (11.9)	37.7 (13.5)	40.8 (16.4)
Disease duration (mean, SD)	–	22.3 (11.8)	17.5 (12.9)	11.7 (12.8)	9.9 (14.1)	29.5 (11.0)	18.6 (15.9)	8.3 (10.5)	17.8 (13.0)	12.6 (19.7)

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^{^a}

The educational level in UKB was recorded as qualification: (1) college or university degree; (2) A levels/AS levels or NVQ or HND or HNC or equivalent; (3) O levels/GCSEs or equivalent; (4) CSEs or equivalent.

^{^b}

For the CMB group refers to the age at which the first mental disorder was diagnosed.

Abbreviations: ANX, anxiety disorder; APOE, apolipoprotein E; BD, bipolar disorder; BMI, body mass index; CMB, comorbidity of mental disorders; CSE, certificate of secondary education; ED, eating disorder; GCSE, general certificate of secondary education; HNC, higher national certificate; HND, higher national diploma; IQR, interquartile range; MDD, major depressive disorder; NC, non‐condition; NVQ, national vocational qualification; OCD, obsessive‐compulsive disorder; PTSD, post‐traumatic stress disorder; SCZ, schizophrenia; SD, standard deviation; SUD, substance use disorder; TS, Tourette syndrome; UKB UK Biobank.

3.2. Risk of AD with mental disorder history

The risk of developing into AD as well as the AAO are presented in Table 2. The results of analyses on other dementia subtypes are presented in Table S3 in supporting information. Compared to the AR of AD between different exposure groups, we found a significantly increased risk in individuals with a history of MDD (OR = 1.3, P < 0.001). In addition, participants with a history of BD or MDD also presented higher odds of all‐cause dementia, VD, and FTD (Table S3).

TABLE 2.

Risk of Alzheimer's disease in different mental disorders.

Risk measurement	Groups
Risk measurement	NC	SCZ	BD	MDD	ANX	ED	OCD	PTSD	SUD	CMB
AD outcome (N)	3709	5	15	274	51	0	1	1	4	33
AR (%)	0.8	1.2	1.4	1.1	0.9	0	1.7	0.4	1.0	0.6
OR	/	1.5	1.7	1.3 ^†††	1.1	/	2.0	0.5	1.2	0.75
Adjusted HR (95% CI)	/	2.36 (0.98–5.68)	2.37 (1.43–3.94) ^**	1.63 (1.44–1.84) ^***	1.22 (0.92–1.61)	/	2.90 (0.41–20.62)	0.56 (0.08–4.01)	2.04 (0.76–5.44)	1.32 (0.93–1.86)
AAO of AD (mean, SD)	75.7 (5.0)	71.0 (9.4)	74.6 (6.0)	73.9 (5.9) ^§§§	75.8 (4.7)	/	73	76	80 (2.8)	72.9 (7.1) ^§

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Abbreviations: AAO, age at onset; AD, Alzheimer's disease; ANX, anxiety disorder; AR, attack rate; BD, bipolar disorder; CI, confidence interval; CMB, comorbidity of mental disorders; ED, eating disorder; HR, hazard ratio; MDD, major depressive disorder; NC, non‐condition; OCD, obsessive‐compulsive disorder; OR, odds ratio; PTSD, post‐traumatic stress disorder; SCZ, schizophrenia; SD, standard deviation; SUD, substance use disorder; TS, Tourette syndrome.

Fisher exact test:

^††† P < 0.001.

Cox regression analysis:

^** P < 0.01

^*** P < 0.001.

Wilcoxon test:

^§ P < 0.05

^§§§ P < 0.001.

In the Cox regression analysis, after adjusting for confounders such as age, sex, educational level, diabetes, body mass index, smoking status, and the presence of the APOE ε4 allele, individuals with a history of BD (HR = 2.37, P < 0.01) and MDD (HR = 1.63, P < 0.001) exhibited a significantly increased risk of developing AD compared to those without these disorders. Additionally, a marginally higher risk of AD development was observed in individuals with a history of SCZ (HR = 2.36, P = 0.055). Furthermore, the AAO of AD was significantly lower in participants with MDD and CMB compared to the control group (P < 0.001 and P < 0.05, respectively).

3.3. Results of bidirectional two‐sample MR and sensitivity analyses

The results of forward two‐sample MR showed a nominal significant causal association between BD, OCD, and TS with AD, with OR_IVW of 1.098 (95% confidence interval [CI] 1.007–1.197), 1.050 (95% CI 1.010–1.091), and 1.064 (95% CI 1.012–1.119) before FDR correction, respectively (Figure 2). For proxy‐AD, BD and OCD showed a nominal significant causal association with an OR_IVW of 1.121 (95% CI 1.055–1.191) and 1.020 (95% CI 1.001–1.040). After the FDR correction, BD is still significantly associated with a higher risk of proxy‐AD (P _FDR< 0.01), while OCD (P _FDR= 0.078) and TS (P _FDR = 0.078) approach significance, suggesting a potential higher risk of AD. There was no evidence supporting reverse causation between AD and mental disorders (Figures S1 and S2 in supporting information). Genetic correlation analysis conducted via linkage disequilibrium score regression ⁵⁵ further confirmed a significant positive correlation between OCD_TS and AD (rg = 0.24, P < 0.05), and between SCZ and proxy‐AD (rg = 0.06, P < 0.05; Table S4 in supporting information). The genetic IVs of each mental disorder are presented in Table S5 in supporting information, while SNPs excluded due to association with confounding phenotypes are presented in Table S6 in supporting information.

Results of forward two‐sample Mendelian randomization. Individuals with a history of BD, OCD, and OCD_TS exhibit an increased risk for AD with nominal significance. After FDR correction, individual with BD kept a significantly higher risk of proxy‐AD, while OCD and OCD_TS presented borderline significance. AD, Alzheimer's disease; ANX, anxiety disorder; BD, bipolar disorder; ED, eating disorder; FDR, false discovery rate; MDD, major depressive disorder; OCD, obsessive‐compulsive disorder; OCD_TS, Tourette syndrome; OR, odds ratio; PTSD, post‐traumatic stress disorder; SCZ, schizophrenia; SUD_ALC, substance use disorder in alcohol; SUD_OPI, substance use disorder in opioids

The results of sensitivity analyses showed high consistency with the IVW method (Tables S7 and S8 and Figure S3 in supporting information) and are visualized in Figure 3. The causal estimates in different mental disorders presented a similar trend among IVW, weighted mode, weighted median, simple mode, and MR‐Egger regression though there was a lack of statistical significance in some of the sensitivity analyses.

Sensitivity analysis of forward two‐sample MR. Sensitivity analysis of two‐sample MR including IVW, weighted mode, weighted median, simple mode, and MR Egger. After transformation, larger size of circle represents more significant level of P value; the color red represents the risk factor (β > 0) while blue represents the protective factor (β < 0). After FDR correction, individual with BD kept a significantly higher risk of proxy‐AD in both IVW and weighted‐median method, while OCD and OCD_TS presented borderline significance. *P < 0.05; **P < 0.01; + P _FDR< 0.05, ++ P _FDR< 0.01. AD, Alzheimer's disease; ANX, anxiety disorder; BD, bipolar disorder; ED, eating disorder; FDR, false discovery rate; IVW, inverse variance weighting; MDD, major depressive disorder; OCD, obsessive‐compulsive disorder; OCD_TS, Tourette syndrome; OR, odds ratio; PTSD, post‐traumatic stress disorder; SCZ, schizophrenia; SUD_ALC, substance use disorder in alcohol; SUD_OPI, substance use disorder in opioids

The forest plots of the leave‐one‐out analyses of SNPs presented no obvious bias in mental disorders and AD (Figure S4 in supporting information). Funnel plots showed there is no evidence of directional pleiotropy in IVW methods (Figure S5 in supporting information). According to the Q test, there is no conspicuous evidence supporting heterogeneity in the results of all mental disorders. There is also no suggestion of pleiotropy detected by the MR Egger intercept test among exposures. No horizontal pleiotropy was addressed in either the preliminary study or validation of the MR‐PRESSO global test (Tables S9–S14 in supporting information).

3.4. Cognitive impairment features in mental disorders

We further investigated the patterns of cognitive impairment among UKB participants diagnosed with mental disorders, analyzing their performance across various data, including scores, errors made, and time taken to complete the tests.

Participants with SCZ, BD, and MDD showed significantly lower scores than the NC control group across the FI, DS, and PM tests. Additionally, those with OCD and SUD had significantly lower scores on the PM test, while individuals with PTSD exhibited significantly lower scores on the FI test. In contrast, participants with ANX and ED performed similarly to the NC group across all cognitive assessments (Figure 4; Table S15 in supporting information).

Cognitive performance in non‐condition controls and individuals with mental disorders. Individuals diagnosed with schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) exhibited significantly lower scores compared to the non‐condition (NC) control group across fluid intelligence (FI), digit span (DS), and pair‐matching (PM) tests. Furthermore, those with obsessive‐compulsive disorder (OCD) and substance use disorder (SUD) presented significantly lower scores than the NC group in the PM test, whereas individuals with post‐traumatic stress disorder (PTSD) showed significantly lower scores in the FI test. Conversely, individuals with anxiety disorder (ANX) and eating disorder (ED) performed comparably to the NC group in all assessed cognitive tests. CMB, comorbidity of mental disorders; SDS, symbol‐digit substitution; TMT, Trail Making Test

In terms of test completion times (measured in deciseconds) and errors (instances), individuals with SCZ, BD, MDD, and ANX showed significantly longer durations and more errors in the PM3 and PM6 tests. Notably, while performance in the numeric Trail Making Test (nTMT) was comparable between OCD individuals and the NC group, those with OCD displayed significantly longer completion times and more errors in the more complex alphanumeric Trail Making Test (aTMT). Interestingly, individuals with SCZ tend to take longer but maintain higher accuracy and make fewer errors than the NC group in both nTMT and aTMT tests (Figure 5 and Table S16 in supporting information).

Duration and errors in cognitive tests among non‐condition (NC) controls and individuals with mental disorders. A, Mean duration to complete each cognitive test (measured by deciseconds). B, Mean total errors made in each cognitive test (measured by instances). Individuals diagnosed with schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), and anxiety disorder (ANX) exhibited significantly longer durations and higher errors in the 3‐card pair‐matching (PM3) and 6‐card pair‐matching (PM6) tests than NC controls. Notably, individuals with obsessive‐compulsive disorder (OCD) demonstrated significantly longer durations and increased errors in the alphanumeric Trail‐Making Test (aTMT). In contrast, individuals with SCZ showed longer durations but fewer errors in both the numeric trail‐making task (nTMT) and aTMT tests. CMB, comorbidity of mental disorders; ED, eating disorder; PTSD, post‐traumatic stress disorder; SDS, symbol‐digit substitution; SUD, substance use disorder

4. DISCUSSION

To our knowledge, this is the most comprehensive study that investigates the risk of dementia and the cognitive impairment features in mental disorders, integrating traditional epidemiological analyses with a two‐sample MR approach. Our findings establish a variable degree of association between several mental disorders (i.e., SCZ, BD, MDD, and OCD) and AD. Significantly, both Cox regression and MR analyses confirmed a link between BD and AD, suggesting shared etiological pathways with dementia. Moreover, we observed distinct cognitive impairment patterns across different mental disorders. These results underscore the negative impact of mental disorders on cognitive functions prior to a dementia diagnosis, highlighting the importance of enhancing the relevant cognitive domains in patients with mental disorders, especially in SCZ, BD, and OCD, to lower the risk of AD. Early detection of cognitive impairment in mental disorders also has some implications for the prevention of dementia.

Results from the observational study indicated that individuals with a history of BD and MDD had a significantly higher risk of developing AD, compared to other types of mental disorders as well as NC groups. These results are consistent with previous studies that have found an increased risk of dementia in individuals with a history of BD and MDD. ⁵⁶ , ⁵⁷ , ⁵⁸ These mental disorders may impair cognitive functioning or increase the vulnerability to neurodegeneration through various mechanisms, such as inflammation, oxidative stress, or vascular damage. ⁵⁹ , ⁶⁰ , ⁶¹

Two‐sample MR further corroborated that BD had a significant causal effect on AD, as indicated by the positive and significant OR_IVW. The effect kept statistical significance after the FDR correction, and the findings were consistent across different sensitivity analyses without the existence of heterogeneity or pleiotropy.

Previous research has highlighted potential molecular and clinical links between BD and AD. Nassan et al. identified genetic markers in early‐onset BD, particularly the rs114034759 risk allele, which is linked to reduced hippocampal expression of the muskelin gene (MKLN1) and may increase surface gamma‐aminobutyric acid (GABA‐A) receptors in the hippocampus. ⁶² This genetic alteration could heighten neuronal excitability, suggesting a connection between BD's hippocampal hyperexcitability and AD through shared GABAergic dysregulation and hippocampal dysfunction. Further, studies have shown that lithium treatment can reverse this hyperexcitability in neurons from BD patients, as demonstrated in models using induced pluripotent stem cells (iPSC). ⁶³ Lithium's role in AD treatment has gained interest, with recent network meta‐analyses indicating it as a cost‐effective alternative to newer agents like aducanumab for treating mild cognitive impairment (MCI) and AD. ⁶⁴ In assessments like the Mini‐Mental State Examination (MMSE), lithium has outperformed other treatments, including donanemab, aducanumab, and placebo. ⁶⁵ Moreover, low‐dose lithium offers a safer profile, enhancing its viability as a long‐term treatment for MCI and AD due to its tolerability and acceptability. ⁶⁵ Collectively, these findings underscore the intertwined molecular and clinical pathways between BD and AD, reinforcing the therapeutic potential of lithium.

Interestingly, OCD spectrum disorders, including OCD and TS, were found to have a significant causal relationship with AD. This association was not statistically significant in the observational study, likely due to the limited sample size of OCD patients in the UKB cohort. Disorders of the basal ganglia, such as TS, Huntington's disease, and tic disorder, often exhibit comorbid OCD symptoms. ⁶⁶ , ⁶⁷ TS is increasingly viewed as part of the OCD spectrum, given its similar clinical features and patterns of familial transmission. ⁶⁸ , ⁶⁹ Both conditions showed a consistent association with AD in MR analysis, strongly suggesting a novel linkage between OCD spectrum disorders and neurodegenerative disease. Current biological hypotheses for OCD spectrum disorders, based on neuroimaging studies, point to functional and structural changes in neural circuits, including the orbitofrontal–subcortical and prefrontal–striatal circuits, which are implicated in cognitive networks related to spatial and attentional cognition. ⁷⁰ , ⁷¹ Additionally, molecular mechanisms involving abnormalities in the serotonergic, dopaminergic, and glutamatergic systems have been proposed. ⁷² However, the exact nature of how these etiological pathways may be connected to AD pathogenesis requires further investigation.

The cognitive impairment results indicate that individuals with SCZ or BD experience impairments across multiple cognitive domains, with distinct patterns of cognitive decline that align with previous findings of deficits in reasoning, memory, and executive function for these disorders. ⁷³ , ⁷⁴ Additionally, our results highlight that individuals with OCD may exhibit slight impairments in complex executive functions without significant deficits in other cognitive domains, consistent with previous reports of executive dysfunction in OCD, as evidenced by performance on the Wisconsin Card Sorting Test (WCST). ⁷⁵ , ⁷⁶

Such distinct patterns in cognitive impairment could be partially explained by the neurophysiology of these mental disorders. According to the results of the UKB observational study, individuals with SCZ typically require significantly longer to complete cognitive tasks than controls. This could be partially due to neural connectivity deficits in SCZ patients, particularly in the prefrontal and medial–temporal cortex, as well as the aberrant N‐methyl‐D‐aspartic acid–mediated synaptic plasticity. ⁷⁷ , ⁷⁸ , ⁷⁹ Extended task completion times may reflect difficulties in initiating movements and execution, consistent with prefrontal lobe dysfunction. Participants with BD exhibit significantly more errors and longer completion times on PM tasks, indicating visual memory impairment. In BD patients, neurofunctional and neurochemical changes, particularly after multiple mood episodes, indicate a progressive degradation in mood‐regulation networks. ⁸⁰ Neuroimaging suggests dysfunction in circuits involving the prefrontal cortex, hippocampus, and amygdala, as well as overactivity in reward‐processing circuitry. ⁸¹ Furthermore, neural hyperexcitability and atrophy in the hippocampus are also noted. ⁶³ , ⁸² The increased errors and extended task times in PM tasks may be linked to hippocampal dysfunction affecting visual memory. OCD patients tend to make more errors in the TMT, particularly in its more complex variants (i.e., alpha‐numeric TMT), suggesting slight impairments in executive function. OCD is associated with abnormalities in cortico–striatal–thalamo–cortical circuits, particularly involving the orbitofrontal cortex and basal ganglia. ⁸³ These abnormalities disrupt the balance between goal‐directed behaviors and habitual responses, contributing to the characteristic compulsive behaviors of OCD. The relatively slight impairments may reflect better cognitive reserve in OCD and a heightened sensitivity to executive tasks requiring more cognitive effort. ⁸⁴

There are potential limitations to this study. First, the diagnosis of AD was not pathologically confirmed by positron emission tomography/computed tomography or cerebrospinal fluid (CSF) biomarkers because these procedures are invasive with high economic costs and ethical challenges. Larger cohorts with non‐invasive biomarkers that can provide a pathological diagnosis of AD should be applied to further confirm these results in the future. ⁸⁵ Second, because higher genetic liability for AD was associated with medical history and cognition as early as midlife, which was largely driven by the APOE gene, there is the possibility that the associations may be a result of prodromal disease or selection bias. ⁸⁶ , ⁸⁷ Last, there is potential sample overlap among ED, PTSD, SUD_ALC, and proxy‐AD due to the inclusion of common samples from the UKB. Consequently, the associations between these diseases and proxy‐AD need to be further verified using updated GWAS datasets that exclude UKB samples.

In conclusion, our findings lend robust support to the hypothesis that various mental disorders exhibit distinct cognitive impairments and elevate the risk of AD, with certain associations proving to be causal. Notably, the consistent association between BD and AD, as demonstrated through epidemiological studies and MR analyses, strongly suggests shared genetic pathways or mechanisms with AD, heightening susceptibility to neurodegeneration and cognitive decline. These findings indicated a potential commonality in the mechanism underlying specific mental disorders and neurodegenerative processes. Additionally, the detailed exploration of cognitive impairment patterns reveals unique profiles across different mental disorders. The identification of these subtle distinctions through more complex tasks highlights the critical need for tailored interventions, informed by a deep understanding of the cognitive deficits specific to each disorder. Future research at the molecular and genetic levels is essential to further elucidate the pathogenesis of AD, paving the way for more effective prevention and treatment strategies.

CONFLICT OF INTEREST STATEMENT

All authors declare no biomedical financial interests or potential competing interests. Author disclosures are available in the supporting information.

CONSENT STATEMENT

All participants provided informed consent in the UK Biobank project.

Supporting information

Supporting Information

ALZ-20-4841-s002.pdf^{(1.8MB, pdf)}

Supporting Information

ALZ-20-4841-s003.docx^{(2.9MB, docx)}

Supporting Information

ALZ-20-4841-s001.pdf^{(675.8KB, pdf)}

ACKNOWLEDGMENTS

Bin Jiao had full access to all data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. The study was conceived and designed by Yiliang Liu, Xuewen Xiao, Lu Shen, and Bin Jiao. The data were analyzed by Yiliang Liu, Xuewen Xiao, Yang Yang, and Rui Yao. The first draft of the manuscript was prepared by Yiliang Liu. The data were collected by Xuewen Xiao, Qijie Yang, Yuan Zhu, Sizhe Zhang, and Xuan Yang. Bin Jiao supervised the completion of this study. All other authors have revised the manuscript critically for important intellectual content. All the authors read and approved the final version of the manuscript. We are grateful for technical support from the Bioinformatics Center, Xiangya Hospital, Central South University. We would like to thank all the investigators of PGC, EADB, IGAP, ADSP, and UKB for making their GWAS summary datasets publicly available. All data generated or analyzed during this study are included in this published article and its online supplementary material. Further inquiries can be directed to the corresponding author. This study was supported by the National Key R&D Program of China (2023YFC3603700), the National Natural Science Foundation of China (No. U22A20300, 82371434, 82001358), the STI2030‐Major Projects (No.2021ZD0201803), the Science and Technology Innovation Program of Hunan Province (2021RC3028, 2021JJ41046), the China Postdoctoral Science Foundation (2022M723554), the Science and Technology Major Project of Hunan Province (2021SK1020), and the Grant of National Clinical Research Center for Geriatric Disorders, Xiangya Hospital (2022LNJJ16).

Liu Y, Xiao X, Yang Y, et al. The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis. Alzheimer's Dement. 2024;20:4841–4853. 10.1002/alz.14049

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PERMALINK

The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis

Yiliang Liu

Xuewen Xiao

Yang Yang

Rui Yao

Qijie Yang

Yuan Zhu

Xuan Yang

Sizhe Zhang

Lu Shen

Bin Jiao

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. BACKGROUND

2. METHODS

2.1. Participants

2.2. Mental disorders and dementia outcome in UKB

Research in context

2.3. Bidirectional two‐sample MR

2.4. Cognitive impairment characteristic

2.5. Statistical analysis

3. RESULTS

3.1. Demographic characteristics of participants

FIGURE 1.

TABLE 1.

3.2. Risk of AD with mental disorder history

TABLE 2.

3.3. Results of bidirectional two‐sample MR and sensitivity analyses

FIGURE 2.

FIGURE 3.

3.4. Cognitive impairment features in mental disorders

FIGURE 4.

FIGURE 5.

4. DISCUSSION

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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