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. 2025 Jul 1;16:5905. doi: 10.1038/s41467-025-60748-8

Association between self-reported multimorbidity and longitudinal brain Aβ deposition in Alzheimer’s disease

Xian-Le Bu 1,2,3,4,5,✉,#, Wei Zhu 6,#, Qing-Hua Wang 1,2,5, Zhuo-Ting Liu 1,2,5, Yun-Yu Bao 1,2,5, Yu-Di Bai 1,2,5, Jiang-Hui Li 1,2,5, Zhi-Hao Liu 1,2,5, Jia-Ling Zhao 7, Yang Xiang 7, Wang-Sheng Jin 1,2,5, Jun Wang 1,2,5, Xia Lei 6,✉, Yan-Jiang Wang 1,2,3,4,5,✉; for the Alzheimer’s Disease Neuroimaging Initiative
PMCID: PMC12219109  PMID: 40592807

Abstract

Multimorbidity is common in older adults. However, whether multimorbidity accelerates brain beta-amyloid (Aβ) deposition, the molecular driver of Alzheimer’s disease (AD), in humans remains largely unknown. In this study, we selected 435 brain Aβ-positive participants with available longitudinal Aβ-PET data (mean duration 3.9 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Twenty-two self-reported chronic disorders were considered as a measure of the severity of multimorbidity. After adjustment for age, sex, education level, APOE-ε4 status and baseline cognitive state, individuals with a high or medium multimorbidity burden had faster rates of brain Aβ accumulation than individuals with a low multimorbidity burden. Moreover, both the central nervous system and peripheral system multimorbidity burdens were associated with longitudinal brain Aβ deposition. These results indicate that peripheral organ and tissue dysfunctions may contribute to AD pathogenesis, which may help researchers better understand AD pathogenesis and tailor interventions for AD from a systemic view.

Subject terms: Alzheimer's disease, Alzheimer's disease

Whether multimorbidity accelerates brain Aβ deposition in humans remains largely unknown. Here, the authors demonstrate that higher self-reported multimorbidity burden predicts increased brain Aβ accumulation rates in the ADNI cohort.

Introduction

Alzheimer’s disease (AD), the most common type of dementia in older adults, is clinically characterized by progressive memory loss and cognitive and behavioral abnormalities. It is estimated that at least 55 million people worldwide suffer from AD or other dementias1. The pathological hallmarks of AD include the accumulation of the abnormal proteins beta-amyloid (Aβ) and phosphorylated tau, as well as the degeneration of neurons in the brain. Aβ is the molecular driver of AD pathogenesis and is considered a promising target for disease-modifying therapies2,3.

AD is conventionally regarded as a brain-specific disease that is independent of the peripheral system or body. Nevertheless, previous studies have reported that the multimorbidity burden is heavy in patients with AD and dementia4,5. Recent large epidemiological studies have demonstrated the associations of systemic multimorbidity with AD and dementia risk6–8, suggesting that multimorbidity may exacerbate the development and progression of AD. Thus, it is speculated that AD may be not only a brain disorder but also a systemic disease involving morbidity in multiple systems beyond the brain9,10. However, the potential mechanism underlying the associations between AD and multimorbidity remains unclear. Recent evidence from our groups and others revealed that abnormalities in non-brain organs and tissues contributed to brain Aβ deposition and cognitive impairment in AD model mice11–16. However, whether AD patients with increased multimorbidity experienced faster Aβ accumulation in the brain is unclear.

In this study, we investigated the impact of the multimorbidity burden on longitudinal brain Aβ deposition in the AD continuum among brain Aβ-positive older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for whom baseline and longitudinal 18F-florbetapir (FBP) Aβ positron emission tomography (PET) scans and medical records were available. The ultimate goal of this study is to obtain evidence from human tissues to clarify the mechanism underlying the increased risk of AD in individuals with a high multimorbidity burden. These findings will help to improve the interventions available for AD patients by incorporating management of multimorbidity, of the brain as well as the peripheral systems.

Results

Characteristics of study participants

A total of 435 brain Aβ-positive participants (153 cognitively unimpaired (CU) and 282 cognitively impaired (CI) participants, CI subdivided into 230 participants with mild cognitive impairment (MCI) and 52 with dementia due to AD) with longitudinal Aβ-PET data from the ADNI database (mean duration 3.9 years) were included. Figure 1 shows the workflow of the study design. We first compared the demographic characteristics of the initial study population and study participants included in the analyses. There were no significant differences in sex, education level, or cognitive diagnoses between the two groups (Table S1). The age and APOE-ε4 allele prevalence were higher in the study population, mainly because all included participants were brain Aβ positive. Overall, these findings imply the representativeness of the study samples and the generalizability of the study results.

Fig. 1. Flow diagram of the study.

Fig. 1

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A Flowchart depicting each step of the selection process for ADNI participant inclusion in the data analyses. B Overview of the study design workflow. ADNI Alzheimer’s Disease Neuroimaging Initiative, AD Alzheimer’s disease, Aβ beta-amyloid, APOE apolipoprotein, CU cognitively unimpaired, FBP 18F-florbetapir, MCI mild cognitive impairment. Created by Figdraw.com.

As shown in Table 1, the included participants had a mean baseline age of 74.6 (SD = 7.1) years; 210 (48.3%) were females, and 264 (60.7%) were apolipoprotein E (APOE) ε4 carriers. To categorize the multimorbidity burden, we binned zero to two, three to five, and six or more coexisting chronic conditions, which could represent low, medium, and high multimorbidity burden, respectively. This strategy has been used in previous studies6,7,17. There were no significant differences in sex, education level, APOE-ε4 allele prevalence, cognitive diagnosis, or baseline brain Aβ standardized uptake value ratios (SUVR) among these groups (Table 1). However, compared with participants with a low multimorbidity burden, participants with a high multimorbidity burden were older and had a longer duration of follow-up, which may potentially introduce bias. Linear mixed-effects models were used to analyze the associations between multimorbidity and annual changes in the brain Aβ SUVR over time. This analytical strategy helps mitigate the influence of varying follow-up durations on the results18.

Table 1.

Demographic and clinical characteristics of brain Aβ-positive participants included in this study

Multimorbidity burden
Total Low (0–2) Medium (3–5) High (≥6) P-value
No., % 435 (100%) 152 (34.9%) 209 (48.1%) 74 (17.0%) /
Age, years (SD) 74.6 (7.1) 73.7 (7.0) 74.5 (7.1) 76.8 (7.1) a 0.008
Female (No., %) 210 (48.3%) 66 (43.4%) 108 (51.7%) 36 (48.6%) 0.300
Education, years (SD) 16.1 (2.7) 16.1 (2.7) 16.1 (2.7) 16.3 (2.8) 0.888
APOE-ε4 (No., %) 264 (60.7%) 98 (64.5%) 127 (60.8%) 39 (52.7%) 0.236
Cognitive diagnosis (No., %) 0.859
 Cognitively unimpaired 153 (35.2%) 51 (33.6%) 76 (36.4%) 26 (35.1%)
 Cognitively impaired 282 (64.8%) 101 (66.4%) 133 (63.6%) 48 (64.9%)
Follow-up, years (SD) 3.90 (2.19) 3.59 (2.03) 3.92 (2.25) 4.47 (2.25) b 0.016
Baseline brain Aβ SUVR (SD) 1.35 (0.18) 1.37 (0.18) 1.34 (0.17) 1.33 (0.20) 0.208
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Aβ beta-amyloid, APOE apolipoprotein, No. number, SD standard deviation, SUVR standardized uptake value ratios. The continuous variables were presented as the mean ± standard deviation (SD) where appropriate and were compared between groups via one-way analysis of variance (ANOVA). The categorical data were summarized as absolute frequencies and were compared between groups via the chi-square test. Two-sided P < 0.05 was used to define statistical significance. aP < 0.05 compared to participants with low and medium multimorbidity burden. bP < 0.05 compared to participants with low multimorbidity burden.

Association of overall multimorbidity burden with baseline and longitudinal brain Aβ deposition

We first analyzed the associations of individual chronic diseases with longitudinal brain Aβ deposition. Participants with hypertension, hyperlipidemia, atrial fibrillation, coronary heart disease, anemia, hearing loss, cancer, and depression presented significantly faster rates of longitudinal brain Aβ accumulation after adjustment for age, sex, education level, APOE-ε4 status and baseline cognitive state (i.e. cognitively unimpaired or cognitively impaired) (Table S2). We subsequently analyzed the associations of multimorbidity burden with brain Aβ deposition. At baseline, age, APOE-ε4 and impaired cognition were related to higher brain Aβ SUVR (Table 2). Compared with individuals with low multimorbidity burden, individuals with a high multimorbidity burden or medium multimorbidity burden were not associated with the baseline brain Aβ SUVR. During the follow-up period, age, APOE-ε4 and impaired cognition were independently associated with Aβ accumulation in the brain (Table 2). Individuals with either medium or high multimorbidity burden presented significantly faster rates of brain Aβ accumulation than did those with a low multimorbidity burden after adjustment for age, sex, education level, APOE-ε4 status, and baseline cognitive state and correction for multiple comparisons (Table 2 and Fig. 2A).

Table 2.

Association of overall multimorbidity burden with baseline and longitudinal brain Aβ deposition

Baseline brain Aβ SUVR
β [95% confidence interval] P-value
Age 0.004 [0.002, 0.006] 0.001
Sex [Female] 0.015 [−0.019, 0.049] 0.375
Education −0.002 [−0.008, 0.004] 0.464
APOE-ε4 0.050 [0.016, 0.085] 0.004
Cognitive diagnosis [CI] 0.097 [0.061, 0.132] <0.001
Multimorbidity burden
 High burden (≥6 chronic disorders, n = 74) −0.042 [−0.090, 0.006] 0.317*
 Medium burden (3–5 chronic disorders, n = 209) −0.028 [−0.064, 0.008] 0.330*
 Low burden (0-2 chronic disorders, n = 152) 0[reference] /
Longitudinal brain Aβ SUVR
β [95% confidence interval] P-value
Time 0.012 [0.008, 0.017] <0.001
Age 0.004 [0.002, 0.005] <0.001
Sex [Female] 0.018 [−0.005, 0.038] 0.128
Education −0.001 [−0.005, 0.002] 0.504
APOE-ε4 0.054 [0.033, 0.076] <0.001
Cognitive diagnosis [CI] 0.071 [0.049, 0.093] <0.001
Time*Multimorbidity burden
 Time*High burden (≥6 chronic disorders) 0.028 [0.015, 0.0401] 0.002*
 Time*Medium burden (3–5 chronic disorders) 0.012 [0.002, 0.021] 0.048*
 Time*Low burden (0–2 chronic disorders) 0[reference] /
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Aβ beta-amyloid, APOE apolipoprotein, CI cognitively impaired, SUVR standardized uptake value ratios. The associations of the multimorbidity with baseline brain Aβ SUVR are analyzed via generalized linear models controlling for age, sex, education level, APOE ε4 status, and baseline cognitive state. Linear mixed-effects models were used to investigate how multimorbidity is associated with longitudinal changes in the brain Aβ SUVR over time, adjusting for age, sex, education level, APOE-ε4 status and baseline cognitive state. *Represented the adjusted p-value obtained by the Bonferroni method’s multiple comparisons correction.

Fig. 2. Longitudinal mixed effects model of the multimorbidity-by-time interaction demonstrated a greater rate of longitudinal brain Aβ accumulation in participants with a greater multimorbidity burden.

Fig. 2

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A Compared with individuals with a low multimorbidity burden of all chronic diseases (0 to 2 chronic disorders), individuals with a medium multimorbidity burden (3 to 5 chronic disorders) and a high multimorbidity burden (6 or more chronic disorders) presented faster rates of Aβ accumulation in the brain during follow-up. B Compared with individuals without chronic disease of central nervous system (0 chronic disorder), individuals with 1 or more chronic disorders presented faster rates of brain Aβ accumulation during follow-up. C Compared with individuals with 0 or 1 chronic disorder of peripheral system, individuals with 2 or more chronic disorders presented faster rates of brain Aβ accumulation during follow-up. The tables show the number of participants available at each time point. Longitudinal brain Aβ SUVR at each follow-up time point were used to generate these figures, which were analyzed via linear mixed effects models adjusted for age, sex, education level, APOE-ε4 status and baseline cognitive state. The lines represent model fits (shaded areas represent the 95% confidence intervals of the regression lines).

Association between the central nervous system multimorbidity burden and longitudinal brain Aβ deposition

Recent studies have shown that numerous chronic disorders in the peripheral system are associated with AD risk7,19,20. Therefore, we performed further analyses with the separation of the multimorbidity burden into two patterns: multimorbidity of the central nervous system (CNS) and multimorbidity of the peripheral system. In order to investigate the associations of multimorbidity burden of CNS (including cerebrovascular diseases, insomnia, anxiety, depression and head injury) with brain Aβ deposition, participants were stratified into zero, one or more coexisting chronic disorders as approximately equal groups. At baseline, age, APOE-ε4 and impaired cognition were associated with increased brain Aβ SUVR (Table 3). No association was found between CNS multimorbidity burden and the baseline brain Aβ SUVR. Longitudinally, individuals with one or more chronic diseases of CNS presented significantly faster rates of brain Aβ accumulation than did those without CNS multimorbidity burden after adjustment for age, sex, education level, APOE-ε4 status and baseline cognitive state (Table 3 and Fig. 2B).

Table 3.

Association of the multimorbidity burden of the central nervous system with longitudinal brain Aβ deposition

Baseline brain Aβ SUVR
β [95% confidence interval] P-value
Age 0.004[0.001, 0.006] 0.003
Sex [Female] 0.013[−0.022, 0.047] 0.469
Education −0.003[-0.009, 0.004] 0.420
APOE-ε4 0.053[0.019, 0.088] 0.003
Cognitive diagnosis [CI] 0.096[0.060, 0.132] <0.001
Multimorbidity burden
 ≥1 chronic disorders (n = 231) −0.001[−0.035, 0.032] 0.933
 0 chronic disorder (n = 204) 0[reference] /
Longitudinal brain Aβ SUVR
β [95% confidence interval] P-value
Time 0.012[0.008, 0.016] <0.001
Age 0.004[0.002, 0.005] <0.001
Sex [Female] 0.011[−0.010, 0.033] 0.303
Education −0.001[−0.003, 0.005] 0.588
APOE-ε4 0.053[0.031, 0.074] <0.001
Cognitive diagnosis [CI] 0.066[0.044, 0.088] <0.001
Time*Multimorbidity burden
 Time*Burden (≥1 chronic disorders) 0.013[0.004, 0.021] 0.003
 Time*Burden (0 chronic disorder) 0[reference] /
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Aβ beta-amyloid, APOE apolipoprotein, CI cognitively impaired, SD standard deviation, SUVR standardized uptake value ratios. The associations of the multimorbidity with baseline brain Aβ SUVR are analyzed via generalized linear models controlling for age, sex, education level, APOE ε4 status, and baseline cognitive state. Linear mixed-effects models were used to investigate how multimorbidity is associated with longitudinal changes in the brain Aβ SUVR over time, adjusting for age, sex, education level, APOE-ε4 status and baseline cognitive state.

Association between the multimorbidity burden of the peripheral system and longitudinal brain Aβ deposition

To investigate the associations of the multimorbidity burden of the peripheral system (including hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, coronary heart diseases, anemia, hypothyroidism, inflammatory skin diseases, anoxic pulmonary diseases, chronic kidney disease, hepatitis, osteoporosis, hearing loss, cancer, gastrointestinal disorders, cataracts, obesity) with brain Aβ deposition, participants were stratified into groups with zero or one, two or more coexisting chronic disorders as approximately equal groups. As shown in Table 4, age, APOE-ε4, and impaired cognition were associated with the baseline brain Aβ SUVR. No associations were found between the peripheral system multimorbidity burden and baseline brain Aβ SUVR. Participants with two or more coexisting chronic disorders showed significantly faster rates of brain Aβ accumulation than those with zero or one chronic disorder after adjustment for potential confounders (Table 4 and Fig. 2C).

Table 4.

Association of the multimorbidity burden of the peripheral system with longitudinal brain Aβ deposition

Baseline brain Aβ SUVR
β [95% confidence interval] P-value
Age 0.004[0.002, 0.006] 0.001
Sex [Female] 0.013[−0.020, 0.047] 0.438
Education −0.002[−0.008, 0.004] 0.496
APOE-ε4 0.050[0.015, 0.084] 0.005
Cognitive diagnosis [CI] 0.094[0.058, 0.129] <0.001
Multimorbidity burden
 ≥2 chronic disorders (n = 198) −0.033[−0.066, 0.001] 0.052
 0 or 1 chronic disorder (n = 237) 0[reference] /
Longitudinal brain Aβ SUVR
β [95% confidence interval] P-value
Time 0.013[0.008, 0.017] <0.001
Age 0.004[0.002, 0.005] <0.001
Sex [Female] 0.017[-0.005, 0.038] 0.128
Education -0.001[-0.005, 0.003] 0.528
APOE-ε4 0.053[0.032, 0.075] <0.001
Cognitive diagnosis [CI] 0.069[0.047, 0.091] <0.001
Time*Multimorbidity burden
 Time*Burden (≥2 chronic disorders) 0.015[0.006, 0.024] <0.001
 Time*Burden (0 or 1 chronic disorder) 0[reference] /
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Aβ beta-amyloid, APOE apolipoprotein, CI cognitively impaired, SD standard deviation, SUVR standardized uptake value ratios. The associations of the multimorbidity with baseline brain Aβ SUVR are analyzed via generalized linear models controlling for age, sex, education level, APOE ε4 status, and baseline cognitive state. Linear mixed-effects models were used to investigate how multimorbidity is associated with longitudinal changes in the brain Aβ SUVR over time, adjusting for age, sex, education level, APOE-ε4 status and baseline cognitive state.

Association between the multimorbidity burden and longitudinal brain Aβ deposition in CU and CI groups

Furthermore, we systematically repeated the association analyses of the multimorbidity burden and brain Aβ deposition in the CU and CI groups. Notably, participants with higher overall multimorbidity burden still presented significantly faster rates of brain Aβ accumulation than did those with lower multimorbidity burden in both the CU and CI groups (Tables 5 and  6). Specifically, in the CU group, participants with a high peripheral system multimorbidity burden exhibited significantly faster rates of brain Aβ accumulation (Table 5). Among participants in the CI group, those with CNS multimorbidity burden also exhibited significantly faster rates of brain Aβ accumulation (Table 6).

Table 5.

Association between the multimorbidity burden and longitudinal brain Aβ deposition in the cognitively unimpaired group

Longitudinal brain Aβ SUVR
Overall multimorbidity
β [95% confidence interval] P-value
Time 0.016[0.009, 0.023] <0.001
Age 0.005[0.002, 0.007] 0.003
Sex [Female] 0.045[0.007, 0.083] 0.021
Education −0.001[−0.007, 0.006] 0.875
APOE-ε4 0.078[0.043, 0.114] <0.001
Time*Multimorbidity burden
 Time*High burden (≥6 chronic disorders, n = 26) 0.032[0.011, 0.053] 0.009*
 Time*Medium burden (3-5 chronic disorders, n = 76) 0.013[−0.002, 0.028] 0.267*
 Time*Low burden (0-2 chronic disorders, n = 51) 0[reference] /
Multimorbidity of central nervous system
β [95% confidence interval] P-value
Time 0.015[0.008, 0.022] <0.001
Age 0.006[0.003, 0.009] <0.001
Sex [Female] 0.039[0.001, 0.078] 0.044
Education −0.001[−0.008, 0.006] 0.700
APOE-ε4 0.079[0.043, 0.114] <0.001
Time*Multimorbidity burden
 Time*Burden (≥1 chronic disorders, n = 64) 0.009[−0.004, 0.023] 0.172
 Time*Burden (0 chronic disorder, n = 89) 0[reference] /
Multimorbidity of peripheral system
β [95% confidence interval] P-value
Time 0.016[0.009, 0.023] <0.001
Age 0.005[0.002, 0.008] 0.003
Sex [Female] 0.046[0.008, 0.084] 0.017
Education −0.001[−0.007, 0.007] 0.919
APOE-ε4 0.076[0.041, 0.112] <0.001
Time*Multimorbidity burden
 Time*Burden (≥2 chronic disorders, n = 87) 0.018[0.004, 0.032] 0.012
 Time*Burden (0 or 1 chronic disorder, n = 66) 0[reference] /
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Aβ beta-amyloid, APOE apolipoprotein, SUVR standardized uptake value ratios. Linear mixed-effects models were used to investigate how multimorbidity is associated with longitudinal changes in the brain Aβ SUVR over time, adjusting for age, sex, education level, APOE-ε4 status and baseline cognitive state. * Represented the adjusted p-value obtained by the Bonferroni method’s multiple comparisons correction.

Table 6.

Association between the multimorbidity burden and longitudinal brain Aβ deposition in cognitively impaired group

Longitudinal brain Aβ SUVR
Overall multimorbidity
β [95% confidence interval] P-value
Time 0.009[0.003, 0.014] 0.004
Age 0.004[0.002, 0.005] <0.001
Sex [Female] 0.002[−0.025, 0.028] 0.899
Education −0.002[−0.007, 0.002] 0.338
APOE-ε4 0.036[0.009, 0.064] 0.010
Time*Multimorbidity burden
 Time*High burden (≥6 chronic disorders, n = 48) 0.025[0.009, 0.041] 0.006*
 Time*Medium burden (3-5 chronic disorders, n = 133) 0.008[-0.005, 0.021] 0.241*
 Time*Low burden (0-2 chronic disorders, n = 101) 0[reference] /
Multimorbidity of central nervous system
β [95% confidence interval] P-value
Time 0.008[0.002, 0.014] 0.006
Age 0.003[0.002, 0.005] <0.001
Sex [Female] −0.003[−0.030, 0.023] 0.810
Education −0.002[−0.007, 0.003] 0.393
APOE-ε4 0.038[0.011, 0.066] 0.006
Time*Multimorbidity burden
 Time*Burden (≥1 chronic disorders, n = 167) 0.018[0.007, 0.030] 0.002
 Time*Burden (0 chronic disorder, n = 115) 0[reference] /
Multimorbidity of peripheral system
β [95% confidence interval] P-value
Time 0.008[0.003, 0.014] 0.004
Age 0.004[0.002, 0.005] <0.001
Sex [Female] 0.001[−0.026, 0.026] 0.969
Education −0.002[−0.007, 0.002] 0.356
APOE-ε4 0.037[0.009, 0.064] 0.009
Time*Multimorbidity burden
 Time*Burden (≥2 chronic disorders, n = 133) 0.010[−0.001, 0.021] 0.087
 Time*Burden (0 or 1 chronic disorder, n = 149) 0[reference] /
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Aβ beta-amyloid, APOE apolipoprotein, SUVR standardized uptake value ratios. Linear mixed-effects models were used to investigate how multimorbidity is associated with longitudinal changes in the brain Aβ SUVR over time, adjusting for age, sex, education level, APOE-ε4 status and baseline cognitive state. *Represented the adjusted p-value obtained by the Bonferroni method’s multiple comparisons correction.

Discussion

In this analysis of 435 Aβ-positive older adults with longitudinal Aβ PET scans from the ADNI followed for nearly 4 years, hypertension, hyperlipidemia, atrial fibrillation, coronary heart disease, anemia, hearing loss, cancer, and depression were related to faster rates of longitudinal Aβ accumulation in the brain. A greater multimorbidity burden was associated with more rapid brain Aβ accumulation. When considering multimorbidity specifically within the CNS or among peripheral systems, those with high multimorbidity at baseline also presented faster longitudinal Aβ deposition. To our knowledge, this study is the first to show that systemic multimorbidity is associated with more rapid longitudinal brain Aβ accumulation, providing novel insights into why multimorbidity increases the risk of AD in older adults.

Numerous studies have addressed the role of specific chronic diseases as risk factors for AD and dementia. Diabetes mellitus, hyperlipidemia, hypertension, obesity, cardio-cerebrovascular diseases, kidney diseases, liver diseases, osteoporosis, skin inflammatory diseases, cancers, cataracts, sleep disorders, and depression can individually increase the risk of AD or dementia8,21–34. Recently, several prospective studies with large sample sizes showed that multimorbidity was associated with a high risk of AD and dementia6–8,35. This finding indicates that the cumulative effect of coexisting chronic diseases drives the association between multimorbidity and incident dementia. However, almost all these studies focused on the associations of chronic conditions with AD risk or cognitive function, whereas few longitudinal studies have investigated the effects of multimorbidity on brain AD-type pathologies. Some cross-sectional studies have shown that multimorbidity is associated with the levels of biomarkers reflecting neurodegeneration in cognitively unimpaired older adults, whereas its association with brain Aβ deposition remained controversial36–38. Thus, why participants with severe multimorbidity have increased AD risk remains largely unknown. It is especially important to know whether older adults with greater multimorbidity burdens experience faster Aβ accumulation in the brain.

In this study, 22 chronic conditions of both the CNS and the peripheral system were analyzed in Aβ-positive older adults. We found that hypertension, hyperlipidemia, atrial fibrillation, coronary heart disease, anemia, hearing loss, cancer, and depression were independently associated with faster rates of longitudinal brain Aβ deposition, whereas other chronic diseases were not. Multiple factors may explain the lack of correlation between dementia risk diseases and brain Aβ deposition. First, several chronic diseases, such as diabetes and chronic kidney disease, are more strongly associated with vascular dementia than with AD39–41. Second, although some individual diseases may not be able to increase brain Aβ deposition independently, their synergistic effects with other morbidities could promote AD pathology. In addition, certain diseases, such as diabetes, gastrointestinal disorders and pulmonary diseases, can promote AD progression through other mechanisms such as energy metabolism, neuroinflammation, and neurodegeneration42–44.

Participants with severe multimorbidity showed more rapid longitudinal brain Aβ accumulation. In addition, multimorbidity of either the CNS or peripheral systems was associated with faster longitudinal Aβ deposition. This finding suggests that the cumulative effects of coexisting chronic diseases drive the association of multimorbidity with brain Aβ deposition. To our knowledge, this longitudinal study provides the first demonstration that greater multimorbidity burden at baseline is associated with faster longitudinal brain Aβ accumulation. Moreover, the association of multimorbidity of the peripheral system with brain Aβ deposition also supports the view of AD as a systemic disease9. The observation that multimorbidity burden is associated with longitudinal Aβ accumulation but not baseline Aβ levels may have several possible explanations. Numerous factors, such as age, cognition, and APOE status, can influence baseline brain Aβ levels, and baseline multimorbidity may not fully represent the previous comorbidities, as some diseases might have been newly diagnosed. Furthermore, substantial literature indicates that many chronic diseases are linked to longitudinal Aβ accumulation rather than baseline Aβ levels20,45–47. Consequently, we propose that the association between multimorbidity burden and longitudinal brain Aβ accumulation may provide a more accurate reflection of the impact of current comorbidities on the pathogenesis of AD. As brain Aβ accumulation begins more than ten years prior to the clinical manifestation of AD dementia48, prevention or timely identification and efficient management of multimorbidity might be important to prevent or delay AD progression.

Multimorbidity may accelerate brain AD pathologies and AD development through different mechanisms. Systemic abnormalities such as hypertension49, hyperlipidemia50,51, diabetes mellitus52, hepatic dysfunction16, renal dysfunction13, gut microbiota disturbance53–55, immune system disorders14,15, sleep disorders35, head injury56, and cerebral hypoperfusion57, have been shown to promote brain Aβ deposition in mice. Cross-sectional studies revealed that hepatic dysfunction58, renal dysfunction59, respiratory disorders29, insomnia60, and cerebral hypoperfusion61,62 were all associated with Aβ deposition in the brain. In our longitudinal cohort study, the analyses of the severity of the multimorbidity burden suggest that the cumulative effects of multiple morbidities accelerated brain Aβ accumulation over time. Various morbidities may share certain pathophysiological mechanisms or risk factors and may have synergistic effects on brain dysfunction. Reduced cerebral blood flow or hypoxia resulting from cardiac, respiratory, or cerebrovascular diseases could lead to blood-brain barrier dysfunction and accelerate Aβ deposition in the brain51,63. In addition to Aβ, multiple other events are involved in the progression of AD. Participants with serve multimorbidity burdens may be more likely to have systemic chronic inflammation6, which is considered to play a significant role in the development of AD64,65. Systemic factors (e.g., plasma proteins, microbial metabolites, and immune cells) mediate brain aging and Aβ pathology in the brain66–69. Therefore, multimorbidity may lead to disturbances in multiple systemic factors and thus promote Aβ deposition in the brain through different mechanisms. In addition, aging is a potential reason for the positive association between multimorbidity burden and brain Aβ accumulation, as participants with higher multimorbidity burdens were more likely to be older. The 2024 AD diagnostic criteria incorporate the assessment of non-AD co-pathologies into disease staging70, highlighting the significant impact of comorbidities on AD progression.

The strengths of our study include the use of Aβ-PET-positive older adults from the longitudinal ADNI cohort with a mean follow-up of 3.9 years. To our knowledge, this study is the first to use a longitudinal framework to examine the association of baseline multimorbidity with longitudinal brain Aβ accumulation. Nevertheless, this study also has some limitations. First, multimorbidity was defined by self-reported medical conditions, which may be less accurate than using objectively confirmed diseases in medical records; verification of those diagnoses with reference to medical records would strengthen the findings. Second, the inclusion criteria for the ADNI cohort exclude certain systemic diseases, and the multimorbidity burden of participants might change during follow-up, so the multimorbidity examined in this study cannot fully represent the multimorbidity burden of patients in the real world. Third, participants lacking demographics or medical history information were excluded from the study, and the reasons for follow-up dropout in ADNI, whether due to refusal or death, was not clearly documented or reported; the use of only cases with complete longitudinal data may have biased the results. Fourth, the impact of medication intake on brain Aβ deposition during follow-up cannot be ruled out. Fifth, owing to the small sample size of participants with longitudinal tau-PET data, we did not analyze the relationship between multimorbidity burden and tau accumulation. This topic needs further study in the future.

In conclusion, we found that a greater multimorbidity burden was associated with more rapid brain Aβ accumulation in the longitudinal ADNI cohort, indicating that dysfunctions in peripheral organs and tissues may be implicated in AD pathogenesis. These findings may help us improve our understanding of AD pathogenesis and tailor interventions for AD from a systemic perspective. The prevention and effective management of multimorbidity in older adults may help prevent or delay AD.

Methods

Study population

The ADNI cohort was assembled in 2004 to track the progression of AD with neuropsychological assessment, biospecimen biomarkers, magnetic resonance imaging (MRI), and PET through the process of normal aging, including the progression of MCI to dementia or AD. The participants included in the ADNI study were aged 55 through 90 years and included various states of cognitive functioning (normal, MCI, and dementia due to AD). The study design and patient inclusion criteria have been described in detail previously71 and are available on the ADNI website (adni.loni.usc.edu). The ADNI study was approved by the institutional review boards of all the participating research centers: Oregon Health and Science University; University of Southern California; University of California-San Diego; University of Michigan; Mayo Clinic, Rochester; Baylor College of Medicine; Columbia University Medical Center; Washington University, St. Louis; University of Alabama at Birmingham; Mount Sinai School of Medicine; Rush University Medical Center; Wien Center; Johns Hopkins University; New York University; Duke University Medical Center; University of Pennsylvania; University of Kentucky; University of Pittsburgh; University of Rochester Medical Center; University of California, Irvine; University of Texas Southwestern Medical School; Emory University; University of Kansas, Medical Center; University of California, Los Angeles; Mayo Clinic, Jacksonville; Indiana University; Yale University School of Medicine; McGill University, Montreal-Jewish General Hospital; Sunnybrook Health Sciences, Ontario; U.B.C.Clinic for AD & Related Disorders; Cognitive Neurology-St. Joseph’s, Ontario; Cleveland Clinic Lou Ruvo Center for Brain Health; Northwestern University; Premiere Research Inst (Palm Beach Neurology); Georgetown University Medical Center; Brigham and Women’s Hospital; Stanford University; Banner Sun Health Research Institute; Boston University; Howard University; Case Western Reserve University; University of California, Davis-Sacramento; Neurological Care of CNY; Parkwood Hospital; University of Wisconsin; University of California, Irvine—BIC; Banner Alzheimer’s Institute; Dent Neurologic Institute; Ohio State University; Albany Medical College; Hartford Hospital, Olin Neuropsychiatry Research Center; Dartmouth-Hitchcock Medical Center; Wake Forest University Health Sciences; Rhode Island Hospital; Butler Hospital; UC San Francisco; Medical University South Carolina; St. Joseph’s Health Care Nathan Kline Institute; University of Iowa College of Medicine; Cornell University; and University of South Florida: USF Health Byrd Alzheimer’s Institute. Informed consent was obtained from the participants (or their authorized representatives) according to the Declaration of Helsinki before study enrollment.

In the present study, we included all participants from the ADNI cohort were brain Aβ positive at baseline and had longitudinal brain FBP Aβ PET data from the ADNI cohort (Aβ PET scans at 2 or more timepoints) available. Participants who were brain Aβ negative at baseline or for whom demographic or medical history information was missing were excluded from the study (refer to Fig. 1). Finally, a total of 435 participants (153 with CU, 230 with MCI, and 52 with dementia due to AD) were included in the data analyses. Demographic and clinical information was collected at baseline. The variables examined included age, sex, education, and APOE genotype. All data used in this study were downloaded from the ADNI website on 9 August 2023.

Multimorbidity

The chronic conditions of the participants in the ADNI cohort were determined according to their self-reported medical history (RECMHIST.csv and MEDHIST.csv files downloaded from the ADNI website). Obesity is defined as a body mass index (BMI) greater than or equal to 30, calculated via the following formula: [weight (kilograms)/height (meters)2]. Height and weight were measured for all ADNI participants (VITALS.csv file downloaded from the ADNI website). Participants and/or caregivers were queried regarding current or previous diseases, which were recorded as follows: system affected, description of the problem, dates of the problem, and whether it was current or resolved. This information is available on the ADNI website (adni.loni.usc.edu). The ADNI database is widely recognized for its comprehensive and well-structured datasets, with medical history being one of its key components. While reliance on self-reported or historically recorded comorbidities may introduce some variability, extensive validation and cross-referencing efforts in high-quality studies have demonstrated its reliability and value in understanding AD progression and related factors20,45,72,73. Chronic conditions were selected according to their possible associations with AD or dementia from previous studies6,7,74, and diseases with a prevalence of less than 1% were excluded. Finally, the presence of 22 chronic conditions was assessed: hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, coronary heart diseases, hypothyroidism, obesity, inflammatory skin diseases, anoxic pulmonary diseases, anemia, chronic kidney disease, hepatitis, gastrointestinal disorders, osteoporosis, cancer, hearing loss, cataracts, cerebrovascular diseases (stroke or transient ischemic attack), insomnia, anxiety, depression, and head injury. Multimorbidity is defined as any combination of chronic disease with at least one other disease in the same individual75,76. Thus, the multimorbidity burdens were categorized as low, medium, and high for zero to two, three to five, and six or more conditions, respectively.

Aβ PET

The FBP Aβ PET data were obtained from the ADNI database (http://adni-info.org). The Aβ PET scans in this study utilized the summary SUVR, which was computed by measuring the lateral/medial frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions. The mean FBP uptake in the whole cerebellum (white and gray matter) was calculated as a reference. Brain Aβ positivity was defined as an FBP SUVR in AD summary cortical regions ≥1.11. For further details, please refer to “UC Berkeley-Amyloid PET Processing Methods.pdf” on the ADNI website.

Statistics

The continuous variables were presented as the mean ± standard deviation (SD) where appropriate and were compared between groups via one-way analysis of variance (ANOVA). The categorical data were summarized as absolute frequencies and were compared between groups via the chi-square test. The Kolmogorov‒Smirnov test and visual examination of the data histograms were used to determine the normality of the distributions. The deviation from linearity test was used to determine the linearity of the data. The associations of the multimorbidity with baseline brain Aβ SUVR were analyzed via generalized linear models controlling for age, sex, education level, APOE ε4 status, and baseline cognitive state based on previous studies18,77. P-values were compared against a Bonferroni-adjusted α when conducting multiple statistical tests between low, medium and high multimorbidity [α = 0.05/number of tests (3)]. The adjusted p-values were calculated by multiplying the uncorrected p-values by 3. If the adjusted p-value was less than 0.05, it indicated a statistically significant difference. Linear mixed-effects models were used to investigate how multimorbidity is associated with longitudinal changes in the brain Aβ SUVR over time, adjusting for age, sex, education level, APOE-ε4 status and baseline cognitive state and using the Bonferroni method’s multiple comparisons correction as mentioned above. SPSS (version 20) and GraphPad Prism (version 9.3) were used for the statistical analyses. Two-sided P < 0.05 was used to define statistical significance.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information (341.8KB, pdf)
Reporting Summary (89KB, pdf)
Transparent Peer Review file (479.6KB, pdf)

Source data

Source Data (13.9KB, xlsx)

Acknowledgements

We gratefully thank all the ADNI participants and staff for their contributions to data acquisition. This work was supported by the National Science Foundation of China (No. 82122023, U22A20294 to X.-L. Bu), the Science and Technology Innovation 2030 Major Projects (No. 2022ZD0211604-1 to X.-L. Bu), the Natural Science Foundation of Chongqing Municipality (No. CSTB2023NSCQ-JQX0019 to X.-L. Bu), and the Project of Sichuan Department of Science and Technology (No. 2023YFS0267 to Y. Xiang, 2022NSFSC1374 to J.-L. Zhao). The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. The Fig. 1 was created with Figdraw.com.

Author contributions

X.-L. Bu, X. Lei and Y.-J. Wang designed the study. X.-L. Bu, W. Zhu, Q.-H. Wang, Z.-T. Liu, and J. Wang conducted the main analyses and drafted the manuscript. X.-L. Bu, W. Zhu, Q.-H. Wang, Z.-T. Liu, Y.-Y. Bao, Y.-D. Bai, J.-H. Li, Z.-H. Liu, J.-L. Zhao, Y. Xiang, and W.-S. Jin contributed to data collection and analyses. X.-L. Bu, W. Zhu, X. Lei, and Y.-J. Wang critically revised the manuscript. All authors reviewed and approved the final version.

Peer review

Peer review information

Nature Communications thanks Meichen Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

Data used in preparation for this study were obtained from the ADNI database (adni.loni.usc.edu) via data sharing agreements. The participant-level original and preprocessed data cannot be made publicly accessible due to restrictions set by the ADNI. All data supporting the findings described in this paper are available within the paper, in the Supplementary Information and from the corresponding author upon reasonable request. Source data are provided with this paper.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Xian-Le Bu, Wei Zhu.

A list of authors and their affiliations appears at the end of the paper.

Contributor Information

Xian-Le Bu, Email: buxianle@sina.cn.

Xia Lei, Email: xialeidpyy@163.com.

Yan-Jiang Wang, Email: yanjiang_wang@tmmu.edu.cn.

for the Alzheimer’s Disease Neuroimaging Initiative:

Michael W. Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowski, Arthur W. Toga, Laurel Beckett, Robert C. Green, John Morris, Leslie M. Shaw, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, John C. Morris, Maria Carroll, Mary L. Creech, Erin Franklin, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, Randall Griffith, David Clark, David Geldmacher, John Brockington, Erik Roberson, Marissa Natelson Love, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla de Toledo-Morrell, Ranjan Duara, Daniel Varon, Maria T. Greig, Peggy Roberts, Marilyn Albert, Chiadi Onyike, Daniel D’Agostino, Stephanie Kiello, James E. Galvin, Brittany Cerbone, Christina A. Michel, Dana M. Pogorelec, Henry Rusinek, Mony J. de Leon, Lidia Glodzik, Susan De Santi, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Ruth A. Mulnard, Gaby Thai, Catherine Mc-Adams-Ortiz, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H. S. Silverman, Po H. Lu, George Bartzokis, Neill R. Graff-Radford, Francine Parfitt, Tracy Kendall, Heather Johnson, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Cynthia Hunt, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Howard Feldman, Benita Mudge, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek Marsel Mesulam, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T.-Y. Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Steven G. Potkin, Adrian Preda, Dana Nguyen, Pierre Tariot, Anna Burke, Nadira Tricic, Adam Fleisher, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Anahita Adeli, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Leslie Gordineer, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Henry Querfurt, Geoffrey Tremont, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebali Ashok Raj, and Kristin Fargher

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-025-60748-8.

References

  • 1.Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet396, 413–446 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Zhang, Y., Chen, H., Li, R., Sterling, K. & Song, W. Amyloid beta-based therapy for Alzheimer’s disease: challenges, successes and future. Signal Transduct. Target Ther.8, 248 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Lian, Y. et al. Clarity on the blazing trail: clearing the way for amyloid-removing therapies for Alzheimer’s disease. Mol. Psychiatry29, 297–305 (2024). [DOI] [PubMed]
  • 4.Bunn, F. et al. Comorbidity and dementia: a scoping review of the literature. BMC Med12, 192 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Wang, Q. H. et al. Comorbidity Burden of Dementia: A Hospital-Based Retrospective Study from 2003 to 2012 in Seven Cities in China. Neurosci. Bull.33, 703–710 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Grande, G. et al. Multimorbidity burden and dementia risk in older adults: The role of inflammation and genetics. Alzheimers Dement17, 768–776 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Ben Hassen, C. et al. Association between age at onset of multimorbidity and incidence of dementia: 30 year follow-up in Whitehall II prospective cohort study. BMJ376, e068005 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Hu, H. Y. et al. Association between multimorbidity status and incident dementia: a prospective cohort study of 245,483 participants. Transl. Psychiatry12, 505 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wang, J., Gu, B. J., Masters, C. L. & Wang, Y. J. A systemic view of Alzheimer disease - insights from amyloid-beta metabolism beyond the brain. Nat. Rev. Neurol.13, 612–623 (2017). [DOI] [PubMed] [Google Scholar]
  • 10.Ullah, R., Park, T. J., Huang, X. & Kim, M. O. Abnormal amyloid beta metabolism in systemic abnormalities and Alzheimer’s pathology: Insights and therapeutic approaches from periphery. Ageing Res Rev.71, 101451 (2021). [DOI] [PubMed] [Google Scholar]
  • 11.Sun, H. L. et al. Blood cell-produced amyloid-beta induces cerebral Alzheimer-type pathologies and behavioral deficits. Mol. Psychiatry26, 5568–5577 (2021). [DOI] [PubMed] [Google Scholar]
  • 12.Chen, C. et al. Gut inflammation triggers C/EBPbeta/delta-secretase-dependent gut-to-brain propagation of Abeta and Tau fibrils in Alzheimer’s disease. EMBO J.40, e106320 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Tian, D. Y. et al. Physiological clearance of amyloid-beta by the kidney and its therapeutic potential for Alzheimer’s disease. Mol. Psychiatry26, 6074–6082 (2021). [DOI] [PubMed] [Google Scholar]
  • 14.Yu, Z. Y. et al. Physiological clearance of Abeta by spleen and splenectomy aggravates Alzheimer-type pathogenesis. Aging Cell21, e13533 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Liu, Z. H. et al. Improving blood monocyte energy metabolism enhances its ability to phagocytose amyloid-beta and prevents alzheimer’s disease-type pathology and cognitive deficits. Neurosci. Bull.39, 1775–1788 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Cheng, Y. et al. Physiological beta-amyloid clearance by the liver and its therapeutic potential for Alzheimer’s disease. Acta Neuropathol.145, 717–731 (2023). [DOI] [PubMed] [Google Scholar]
  • 17.Dalgaard, F. et al. Management of Atrial Fibrillation in Older Patients by Morbidity Burden: Insights From Get With The Guidelines-Atrial Fibrillation. J. Am. Heart Assoc.9, e017024 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Collij, L. E. et al. Lewy body pathology exacerbates brain hypometabolism and cognitive decline in Alzheimer’s disease. Nat. Commun.15, 8061 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Wang, J. et al. Poor pulmonary function is associated with mild cognitive impairment, its progression to dementia, and brain pathologies: A community-based cohort study. Alzheimers Dement18, 2551–2559 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Du, J. et al. Association of APOE-epsilon4, Osteoarthritis, beta-Amyloid, and Tau Accumulation in Primary Motor and Somatosensory Regions in Alzheimer Disease. Neurology101, e40–e49 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Li, J. et al. Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease. Neurology76, 1485–1491 (2011). [DOI] [PubMed] [Google Scholar]
  • 22.Zhu, J., Wang, Y., Li, J., Deng, J. & Zhou, H. Intracranial artery stenosis and progression from mild cognitive impairment to Alzheimer disease. Neurology82, 842–849 (2014). [DOI] [PubMed] [Google Scholar]
  • 23.Zhang, H., Zhang, D., Tang, K. & Sun, Q. The relationship between alzheimer’s disease and skin diseases: a review. Clin. Cosmet. Investig. Dermatol14, 1551–1560 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wang, S. et al. Association of impaired kidney function with dementia and brain pathologies: A community-based cohort study. Alzheimers Dement19, 2765–2773 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Shang, Y., Widman, L. & Hagstrom, H. Nonalcoholic Fatty Liver Disease and Risk of Dementia: A Population-Based Cohort Study. Neurology99, e574–e582 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Xue, M. et al. Diabetes mellitus and risks of cognitive impairment and dementia: A systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev.55, 100944 (2019). [DOI] [PubMed] [Google Scholar]
  • 27.Lary, C. W. et al. Bone mineral density and the risk of incident dementia: A meta-analysis. J. Am. Geriatr. Soc.72, 194–200 (2024). [DOI] [PMC free article] [PubMed]
  • 28.Zhang, Y. et al. Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank. Nat. Hum. Behav.7, 1185–1195 (2023). [DOI] [PubMed] [Google Scholar]
  • 29.Andre, C. et al. Association of sleep-disordered breathing with alzheimer disease biomarkers in community-dwelling older adults: a secondary analysis of a randomized clinical trial. JAMA Neurol.77, 716–724 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Ding, X. et al. Diabetes accelerates Alzheimer’s disease progression in the first year post mild cognitive impairment diagnosis. Alzheimers Dement20, 4583–4593 (2024). [DOI] [PMC free article] [PubMed]
  • 31.Liu, Z. T., Liu, M. H., Xiong, Y., Wang, Y. J. & Bu, X. L. Crosstalk between bone and brain in Alzheimer’s disease: Mechanisms, applications, and perspectives. Alzheimers Dement20, 5720–5739 (2024). [DOI] [PMC free article] [PubMed]
  • 32.Ma, L. Z. et al. Cataract, Cataract Surgery, and Risk of Incident Dementia: A Prospective Cohort Study of 300,823 Participants. Biol. Psychiatry93, 810–819 (2023). [DOI] [PubMed] [Google Scholar]
  • 33.Deng, Y. T. et al. Association of life course adiposity with risk of incident dementia: a prospective cohort study of 322,336 participants. Mol. Psychiatry27, 3385–3395 (2022). [DOI] [PubMed] [Google Scholar]
  • 34.Wang, J. et al. Association of cancer history with structural brain aging markers of Alzheimer’s disease and related dementias risk. Alzheimers Dement20, 880–889 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Parhizkar, S. et al. Sleep deprivation exacerbates microglial reactivity and Abeta deposition in a TREM2-dependent manner in mice. Sci. Transl. Med15, eade6285 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Mendes, A. et al. Multimorbidity is associated with preclinical alzheimer’s disease neuroimaging biomarkers. Dement Geriatr. Cogn. Disord.45, 272–281 (2018). [DOI] [PubMed] [Google Scholar]
  • 37.Vassilaki, M. et al. The Association of Multimorbidity With Preclinical AD Stages and SNAP in Cognitively Unimpaired Persons. J. Gerontol. A Biol. Sci. Med Sci.74, 877–883 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Ren, Y. et al. Multimorbidity, cognitive phenotypes, and Alzheimer’s disease plasma biomarkers in older adults: a population-based study. Alzheimers Dement20, 1550–1561 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Wang, Y. et al. Onset age of diabetes and incident dementia: a prospective cohort study. J. Affect Disord.329, 493–499 (2023). [DOI] [PubMed] [Google Scholar]
  • 40.Litkowski, E. M. et al. Mendelian randomization study of diabetes and dementia in the Million Veteran Program. Alzheimers Dement19, 4367–4376 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Xu, H. et al. Kidney function, kidney function decline, and the risk of dementia in older adults: a registry-based study. Neurology96, e2956–e2965 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lemche, E. et al. Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer’s disease: A systematic review and qualitative meta-analysis. Neurobiol. Dis.196, 106485 (2024). [DOI] [PubMed] [Google Scholar]
  • 43.Huang, Y. et al. The gut microbiome modulates the transformation of microglial subtypes. Mol. Psychiatry28, 1611–1621 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wang, J. et al. Pulmonary function is associated with cognitive decline and structural brain differences. Alzheimers Dement18, 1335–1344 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Wang, H. F. et al. Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology. EBioMedicine86, 104336 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Bubu, O. M. et al. Self-reported obstructive sleep apnea, amyloid and tau burden, and Alzheimer’s disease time-dependent progression. Alzheimers Dement17, 226–245 (2021). [DOI] [PMC free article] [PubMed]
  • 47.Weiner, M. W. et al. Traumatic brain injury and post-traumatic stress disorder are not associated with Alzheimer’s disease pathology measured with biomarkers. Alzheimers Dement19, 884–895 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Jia, J. et al. Biomarker Changes during 20 Years Preceding Alzheimer’s Disease. N. Engl. J. Med390, 712–722 (2024). [DOI] [PubMed] [Google Scholar]
  • 49.Faraco, G. et al. Hypertension enhances Abeta-induced neurovascular dysfunction, promotes beta-secretase activity, and leads to amyloidogenic processing of APP. J. Cereb. Blood Flow. Metab.36, 241–252 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Julien, C. et al. High-fat diet aggravates amyloid-beta and tau pathologies in the 3xTg-AD mouse model. Neurobiol. Aging31, 1516–1531 (2010). [DOI] [PubMed] [Google Scholar]
  • 51.Alexander, C. et al. Hypoxia Inducible Factor-1alpha binds and activates gamma-secretase for Abeta production under hypoxia and cerebral hypoperfusion. Mol. Psychiatry27, 4264–4273 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Moreno-Gonzalez, I. et al. Molecular interaction between type 2 diabetes and Alzheimer’s disease through cross-seeding of protein misfolding. Mol. Psychiatry22, 1327–1334 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Chen, C. et al. Gut microbiota regulate Alzheimer’s disease pathologies and cognitive disorders via PUFA-associated neuroinflammation. Gut71, 2233–2252 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Zhang, Y. et al. Transmission of Alzheimer’s disease-associated microbiota dysbiosis and its impact on cognitive function: evidence from mice and patients. Mol. Psychiatry28, 4421–4437 (2023). [DOI] [PMC free article] [PubMed]
  • 55.Grabrucker, S. et al. Microbiota from Alzheimer’s patients induce deficits in cognition and hippocampal neurogenesis. Brain146, 4916–4934 (2023). [DOI] [PMC free article] [PubMed]
  • 56.Wu, Z. et al. Traumatic brain injury triggers APP and Tau cleavage by delta-secretase, mediating Alzheimer’s disease pathology. Prog. Neurobiol.185, 101730 (2020). [DOI] [PubMed] [Google Scholar]
  • 57.ElAli, A., Theriault, P., Prefontaine, P. & Rivest, S. Mild chronic cerebral hypoperfusion induces neurovascular dysfunction, triggering peripheral beta-amyloid brain entry and aggregation. Acta Neuropathol. Commun.1, 75 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Nho, K. et al. Association of Altered Liver Enzymes With Alzheimer Disease Diagnosis, Cognition, Neuroimaging Measures, and Cerebrospinal Fluid Biomarkers. JAMA Netw. Open2, e197978 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Sun, H. L. et al. Associations of blood and cerebrospinal fluid abeta and tau levels with renal function. Mol. Neurobiol.60, 5343–5351 (2023). [DOI] [PubMed] [Google Scholar]
  • 60.Insel, P. S., Mohlenhoff, B. S., Neylan, T. C., Krystal, A. D. & Mackin, R. S. Association of Sleep and beta-Amyloid Pathology Among Older Cognitively Unimpaired Adults. JAMA Netw. Open4, e2117573 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Lee, Y. G., Jeon, S., Kang, S. W. & Ye, B. S. Effects of amyloid beta and dopaminergic depletion on perfusion and clinical symptoms. Alzheimers Dement19, 5719–5729 (2023). [DOI] [PubMed]
  • 62.Okamoto, Y. et al. Cerebral hypoperfusion accelerates cerebral amyloid angiopathy and promotes cortical microinfarcts. Acta Neuropathol.123, 381–394 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Wang, L. et al. Chronic cerebral hypoperfusion induces memory deficits and facilitates Abeta generation in C57BL/6J mice. Exp. Neurol.283, 353–364 (2016). [DOI] [PubMed] [Google Scholar]
  • 64.Liu, Y. H. et al. Immunity and Alzheimer’s disease: immunological perspectives on the development of novel therapies. Drug Discov. Today18, 1212–1220 (2013). [DOI] [PubMed] [Google Scholar]
  • 65.Walker, K. A. et al. The role of peripheral inflammatory insults in Alzheimer’s disease: a review and research roadmap. Mol. Neurodegener.18, 37 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Pluvinage, J. V. & Wyss-Coray, T. Systemic factors as mediators of brain homeostasis, ageing and neurodegeneration. Nat. Rev. Neurosci.21, 93–102 (2020). [DOI] [PubMed] [Google Scholar]
  • 67.Xiong, J. et al. FSH blockade improves cognition in mice with Alzheimer’s disease. Nature603, 470–476 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Liu, Z. H., Wang, Y. J. & Bu, X. L. Alzheimer’s disease: targeting the peripheral circulation. Mol. Neurodegener.18, 3 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Schroer, A. B. et al. Platelet factors attenuate inflammation and rescue cognition in ageing. Nature620, 1071–1079 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Jack, C. R. Jr. et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimers Dement20, 5143–5169 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Aisen, P. S. et al. Clinical core of the Alzheimer’s disease neuroimaging initiative: progress and plans. Alzheimers Dement6, 239–246 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Miller, A. A. et al. Self-reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort. Alzheimers Dement20, 7847–7858 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Hunsberger, H. C. et al. Sex-Specific Effects of Anxiety on Cognition and Activity-Dependent Neural Networks: Insights from (Female) Mice and (Wo)Men. Biol Psychiatry (2024). [DOI] [PMC free article] [PubMed]
  • 74.Selvaraj, S. et al. Association of comorbidity burden with abnormal cardiac mechanics: findings from the HyperGEN study. J. Am. Heart Assoc.3, e000631 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Barnett, K. et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet380, 37–43 (2012). [DOI] [PubMed] [Google Scholar]
  • 76.Le Reste, J. Y. et al. The European General Practice Research Network presents a comprehensive definition of multimorbidity in family medicine and long term care, following a systematic review of relevant literature. J. Am. Med Dir. Assoc.14, 319–325 (2013). [DOI] [PubMed] [Google Scholar]
  • 77.Bu, X. L. et al. Associations of plasma soluble CD22 levels with brain amyloid burden and cognitive decline in Alzheimer’s disease. Sci. Adv.8, eabm5667 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Information (341.8KB, pdf)
Reporting Summary (89KB, pdf)
Transparent Peer Review file (479.6KB, pdf)
Source Data (13.9KB, xlsx)

Data Availability Statement

Data used in preparation for this study were obtained from the ADNI database (adni.loni.usc.edu) via data sharing agreements. The participant-level original and preprocessed data cannot be made publicly accessible due to restrictions set by the ADNI. All data supporting the findings described in this paper are available within the paper, in the Supplementary Information and from the corresponding author upon reasonable request. Source data are provided with this paper.

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