Social Networks and Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease Pathology in Cognitively Intact Older Adults: The CABLE Study

Abstract

Background:

Although social networks are deemed as moderators of incident Alzheimer’s disease (AD), few data are available on the mechanism relevant to AD pathology.

Objective:

We aimed to investigate whether social networks affect metabolism of cerebrospinal fluid (CSF) AD biomarkers during early stage and identify modification effects of genetic factor and subjective cognitive decline (SCD).

Methods:

We studied participants from the Chinese Alzheimer’s disease Biomarker and Lifestyle (CABLE) database who received cognition assessments and CSF amyloid-β (Aβ_1–42 and Aβ_1–40) and tau proteins (total-tau [T-tau] and phosphorylated-tau [P-tau]) measurements. The social networks were measured using self-reported questionnaires about social ties. Linear regression models were used.

Results:

Data were analyzed from 886 cognitively intact individuals aged 61.91 years (SD = 10.51), including 295 preclinical AD participants and 591 healthy controls. Social networks were mostly associated with CSF indicators of AD multi-pathologies (low P-tau/Aβ_1–42 and T-tau/Aβ_1–42 and high Aβ_1–42/Aβ_1–40). Significant differences of genetic and cognitive status were observed for CSF indicators, in which associations of social network scores with CSF P-tau and indicators of multi-pathologies appeared stronger in APOE 4 carriers (versus non-carriers) and participants with SCD (versus controls), respectively. Alternatively, more pronounced associations for CSF T-tau (β= –0.005, p < 0.001), Aβ_1–42/Aβ_1–40 (β= 0.481, p = 0.001), and T-tau/Aβ_1–42 (β= –0.047, p < 0.001) were noted in preclinical AD stage than controls.

Conclusion:

These findings consolidated strong links between social networks and AD risks. Social networks as a modifiable lifestyle probably affected metabolisms of multiple AD pathologies, especially among at-risk populations.

Keywords

Alzheimer’s disease biomarkers cerebrospinal fluid lifestyle social network

INTRODUCTION

Alzheimer’s disease (AD) is characterized by accumulations of extracellular protein amyloid-β peptides (Aβ) and intracellular pathological form of tau protein, which involve abnormal neuron-to-neuron communication and transport of molecules inside neurons [1]. The delay of averaged two-year onset by the year of 2050 could reduce the worldwide prevalence of AD by 22.8 million, which would substantially mitigate the influence of symptom-driven physical and psychological problems [2]. Therefore, concerning research has attached the utmost importance to the determination of modifiable risk factors to delay the symptomatic onset and especially the occurrence of AD pathology [3].

Social networks refer to webs of social ties (such as friends and acquaintances) connected by interpersonal relationships; establishment of them probably facilitate social engagement and access to social support in turn [4]. Elements of social networks incorporate network size and the frequency of contact between members of social networks, and they all represent structural aspects of social relationships [4, 5]. The impoverished social network is a cause of concern because it fosters multiple adverse health outcomes, such as disability, mortality, and quality of life [6, 7]. In contrary, multiple social ties to friends, neighbors, and family are rewarding for longevity [8]. In addition, research has connected social network diversity to the depression, anxiety, and distress, which all boost neurobiological alterations of AD pathology [8]. Emerging evidence has led to a general acceptance that this psychosocial factor intervenes the incidences of dementia and cognitive decline. This evidence explicitly suggests infrequent social contact and few social ties increase the risk of AD and aggravated cognitive deficits [2 , 9–11]. However, the neurobiological alterations linked with the social network in the neurodegenerative process remain ambiguous, and there is a lack of large-scale studies engaging in underlying pathology mechanisms. Several studies identified some involving the production of amyloid-β (Aβ) peptide and tau protein, oxidative stress, inflammatory reaction, synaptic plasticity, and myelination [12]. Brain autopsy and clinical Pittsburgh Compound B (PiB)-PET studies have yielded inconsistent findings on the association of social relationships with the amyloid burden and neurofibrillary tangles [13, 14].

The cerebrospinal fluid (CSF) amyloid-β 42 (Aβ_1–42), total tau (T-tau), and phosphorylated tau (P-tau), and their ratios have been putative as biomarkers of AD pathology (amyloid deposition, tau pathology, and neuronal injury [15]) for their best predictive and diagnostic accuracy of cognitive impairment [15 –18], and candidate biomarkers of early coincident preclinical changes [16 , 20]. Therefore, we aim to ascertain whether social networks affect early metabolisms of cerebral AD core pathology as measured by CSF biomarkers in an ongoing, population-based Chinese Alzheimer’s Biomarker and LifestylE (CABLE) cohort study. We examined the contributions of different social ties to the abnormal levels of CSF AD biomarkers and assessed the combined effect of social networks. In addition, a fast clinical progression over time is clarified among cognitive normal subjects with evidence of AD pathology (namely preclinical AD) and those have subjective cognitive decline (SCD) [21 –24]. We thus further explored whether these identified correlations were more pronounced on at-risk populations (such as preclinical AD and SCD individuals) so as to strengthen favorable acts of social networks even before a phase manifesting evident cognitive decline.

METHODS

CABLE study

CABLE is an ongoing large-scale cohort study initiated in 2017, mainly focusing on AD biomarkers and risk factors in the Chinese Han population [25, 26]. CABLE aims to identify the genetic and environmental modifiers for AD thereby advancing disease prevention and early diagnosis. All participants were administered clinical and neuropsychological assessments, blood and CSF sample collection, as well as biochemical testing. Demographic profile, possible risk factors of AD, and medical history data were acquired by comprehensive questionnaires and electronic medical record systems. All enrolled participants were Han Chinese aged between 40 to 90 years. The participants were excluded if they were diagnosed with: 1) epilepsy, multiple sclerosis, central nervous system infection, or other major neurological disorders; 2) major psychological disorders (e.g., severe depression, psychotic disorders); 3) severe systemic diseases that may influence neurochemical biomarkers of AD; 4) family history of genetic disease [25]. The protocol for the CABLE cohort in compliance with the Helsinki declaration was reviewed and approved by the Institutional Ethics Committee of Qingdao Municipal Hospital. All participants provided written informed consent or assent with proxy consent. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Included participants

A total of 1,273 cognitively intact individuals who received systematic cognition assessments were initiatively recruited as part of the CABLE study, which may include a subset of individuals with abnormal CSF AD biomarkers characteristic of preclinical AD. This setting allows seeing modification impacts of social networks on these neurobiological alterations of AD pathology in an early stage. The general cognitive functions of included participants were evaluated by the recommended Subjective Cognitive Decline Initiative (SCD-I) basic scale [23, 27], the China-modified Mini-Mental State Examination (CM-MMSE) [25 , 29], and the Montreal Cognitive Assessment (MoCA) [30]. Potential depressive symptoms were additionally assessed by the 17-item Hamilton depression rating scale, consistent with previous studies demonstrating abnormal depressive events using cutoffs higher than seven scores [31]. We followed the diagnostic recommendations incorporated in the National Institute on Aging–Alzheimer’s Association (NIA-AA) workgroup reports for the diagnosis of preclinical AD [32]. The cognitive status of SCD was determined according to the recommendation of the SCD-I Working Group [23, 27]. Like preclinical AD, SCD manifesting before cognitive impairment could act as a target population for early intervention trials in AD [24]. In addition, SCD is a harbinger of greater rates of clinical progression in preclinical AD [33]. All support promising evidence reflecting the at-risk nature of the SCD group. Therefore, we not only pay attention to a population during the preclinical AD stage but target SCD individuals expecting to advance perceptions of regulation mechanisms covering all risk groups. Each participant received a consensus diagnosis by the professional medical doctors through intact performance on neuropsychological testing, combined with CSF biomarkers and brain magnetic resonance imaging (MRI) examinations. After removal of extreme outliers, finally, 886 participants who had available social network and CSF biomarkers data were included in this cross-sectional study, which consisted of SCD individuals and non-SCD controls. Total of them were further grouped as preclinical AD subjects (n = 295) and healthy controls (n = 591) (Supplementary Figure 1).

The assessment of social networks

The social networks were measured using self-reported questionnaires about social ties (with friends, relatives, and neighbors) [11]. This study evaluated the closeness of every social tie by recording how subjects got along with their friends, relatives, and neighbors. Subjective options for the closeness in these questionnaires included distant, moderate, and close social ties, which were scored zero, one point, and two points in order. Social ties rating was completed in a blinded manner, regardless of other assessments and procedures. Also, the sum of scores of three social ties was adopted to construct a social network score ranging from 0 to 6 points, with a higher score indicating a closer social relationship [7 , 11].

Measurements of CSF AD biomarkers

CSF was collected in 10 ml polypropylene tubes by lumbar puncture in the morning after overnight fasting and was gently mixed to avoid gradient effects. The samples were centrifuged at 2000 g for 10 min, snap-frozen, and stored at –80°C until assay. The separation and storage of samples were required within 2 h after standard lumbar puncture. Aβ_1–42, Aβ_1–40, T-tau, and P-tau were detected by the enzyme-linked immunosorbent assay (ELISA) kit (Innotest β-AMYLOID (1–42), β-AMYLOID (1–40), PHOSPHO-TAU (181p), and hTAU-Ag; Fujirebio, Ghent, Belgium) on the microplate reader (Thermo Scientific™ Multiskan™ MK3). The standards and samples were detected in duplicates, and the means of the duplicates were used for the statistical analyses. The mean inter-assay coefficient of variation was controlled under 15% (8.15% for Aβ_1–40, 8.79% for Aβ_1–42, 11.34% for P-tau, and 10.38% for T-tau). The mean intra-assay coefficient of variation was controlled under 10% (3.69% for Aβ_1–40, 5.27% for Aβ_1–42, 2.42% for P-tau, and 4.73% for T-tau). All analyses were performed by board-certified laboratory technicians who were blind to clinical information and diagnoses.

APOE genotyping

DNA was extracted from overnight fasting blood samples with the QIAamp®DNA Blood Mini Kit. And the extracted DNA was separated and stored at –80°C until APOE ɛ4 genotyping was performed. Specific loci related to APOE ɛ4 status (rs7412 and rs429358) were selected for genotyping with restriction fragment length polymorphism (RFLP) technology. The results were dichotomized into APOE ɛ4 carrier or non-carrier status.

Statistical analyses

Demographic characteristics were summarized as mean (SD) for continuous variables and number (column percentage) for categorical variables. p values for comparing the between-group difference using Chi-square tests for categorical variables, as well as using t-test (normally distributed data) or Mann-Whitney U test (skewed distribution) for continuous variables. As for the CSF AD biomarkers, the transformation was performed to achieve or approximate a normally distributed data (Kolmogorov-Smirnov test p > 0.05) via “car” package of R software in case of skewed distribution. Individuals who had a value which was three-fold standard deviation (SD) greater or smaller than the mean value were considered as extreme outliers in statistical data and were removed from this research cohort.

In step 1, we used the original social ties data from questionnaire and first analyzed them as three-category variables in the multiple linear models with age (continuous), sex (female = 0, male = 1), education (continuous), and APOE4 status (“44” or “43” or “42” = 1, other genotypes = 0) as covariates. In step 2, to assess the composite effects of the social networks on CSF AD biomarkers, we analyzed a social network summary index scored as a continuous variable in the linear model with same covariates. Additional factors including history of stroke (yes or no), hypertension (yes or no), diabetes (yes or no), marital status (married = 0, unmarried = 1, divorced = 2, being a widow or widower = 3), living status (living alone or not), physical exercise (regular exerciser or not), depressive symptoms (abnormal or normal), and CM-MMSE (continuous) were further corrected to exam the robustness of primary results and confounding effects. In step 3, we included interaction terms in the models to detect modification effects of sex, genetic factor, and cognition on these associations. Subgroup analyses were further performed based on factors that interacted with social networks to clarify an at-risk population.

We detected whether some underlying relationships of social networks with CSF biomarkers of AD pathology differed in preclinical AD and healthy control groups. In accordance with the biomarker-based definition of preclinical AD, asymptomatic β-amyloidosis (A +) was operationally determined by abnormal CSF assay of Aβ_1–42 and “downstream” neurodegeneration (N +) was defined by abnormal CSF assays of T-tau or P-tau [32, 34]. In the present study, the positive β-amyloidosis group represented preclinical AD [22, 34]. CSF biomarkers were binarized as abnormal and normal according to the finding that approximately one-third of cognitively intact older adults suffered from AD pathology [22 , 36]. Similar distributions were also observed in Asian populations [37, 38]. Details regarding the CSF evidence of AD pathophysiology have been illustrated in our previous CABLE studies. Therefore, the levels of CSF Aβ_1–42 in the lower one-third of the distribution (A + , <116.74 pg/ml) and P-tau (N + , >39.00 pg/ml) or T-tau (N + ,>180.26 pg/ml) in the upper one-third of the distribution were considered as abnormal.

Statistical significance was set at a two-sided p < 0.05. Bonferroni method was used for multiple corrections except where specifically noted. All statistical analyses were performed using stats package in R version 3.5.1 software program.

RESULTS

Characteristics of participants

Table 1 summarizes the demographic characteristics and distributions of CSF biomarkers of AD in the whole participants. The mean age of included cognitively intact participants was 61.91 (SD = 10.51) years. The proportion of males was 58.92%, and the proportion of APOE ɛ4 carriers was 15.44%. As for detailed subcomponents of social ties, individuals that had distant ties with friends were older (p = 0.005) and showed lower years of education (p = 0.009) and lower CM-MMSE scores (p < 0.001) (Supplementary Table 1). There was a marked difference only in CM-MMSE performance regarding social ties with relatives (p = 0.001).

Table 1

Characteristics of included participants in CABLE database

Characteristics	All participants
	(N = 886)^a
Age, y	63 (54–69)
Male, No. (%)	522 (58.92)
Education, year	9 (8–12)
APOEɛ4 carrier status, yes, No. (%)	122 (13.77)
CM-MMSE	28 (27–30)
Living alone, yes, No. (%)	52 (5.87)
Marital status, married, No. (%)	807 (91.08)
Stroke, yes, No. (%)	27 (3.02)
Diabetes mellitus, yes, No. (%)	137 (15.46)
Hypertension, yes, No. (%)	326 (36.79)
Regular exercise, yes, No. (%)	380 (42.89)
HAMD score, mean (SD)	0.53 (1.53)
CSF biomarkers, pg/ml
Aβ_1–42	136.75 (110.53–192.30)
T-tau	154.07 (121.13–198.22)
P-tau	35.38 (30.75–41.41)
Aβ_1–42/Aβ_1–40	0.027 (0.020–0.036)
T-tau/Aβ_1–42	1.046 (0.791–1.355)
P-tau/Aβ_1–42	0.247 (0.192–0.309)
Social ties, close, No. (%)
With friends	556 (62.75)
With relatives	669 (75.51)
With neighbors	683 (77.09)
Social network score	5 (4–6)

CSF, cerebrospinal fluid; Aβ, amyloid-β; APOE ɛ4, apolipoprotein E ɛ4 allele; CM-MMSE, China-modified Mini-Mental State Examination; HAMD, Hamilton Depression Scale; T-tau, total tau; P-tau, phosphorylated tau; SD, standard deviation. ^aData are presented as median (IQR) unless otherwise indicated.

Social networks and CSF biomarkers of AD pathology in total participants

Firstly, we investigated whether social networks could affect the metabolisms of CSF proteins of AD pathology in all participants with normal cognition. Social network subcomponents showed several suggestive (p < 0.05) or significant (<0.008 after Bonferroni correction) associations with the levels of CSF amyloid proteins and indicators of multi-pathologies of AD in the whole participants (Fig. 1). Individual with relatively close social ties with relatives (close: β= 0.0015, p = 0.04) and neighbors (moderate, β= 0.0017, p = 0.001; close, β= 0.0015, p = 0.002) presented a higher level of CSF Aβ_1–42 (Supplementary Table 2). Notably, moderate, or close social ties (with friends, relatives, and neighbors) were associated with higher CSF Aβ_1–42/Aβ_1–40 and lower CSF P-tau/Aβ_1–42 and CSF T-tau/Aβ_1–42 (Supplementary Tables 2-3). No associations were found between social network subcomponents with CSF P-tau and T-tau.

Fig. 1

Correlation coefficient maps for social networks and CSF biomarkers of AD core pathology. The social networks showed several suggestive or significant associations with CSF indicators of multiple pathologies of AD in the whole participants, especially in APOE ɛ4 carriers and subjects with SCD. The color-coding and the number in each cell represent the transformed correlation coefficient between social networks and CSF biomarker of AD pathology. The absolute value in each cell represents the log-transformed absolute coefficient. Positive or negative number respectively reflect possible positive or negative correlation inferred from the linear model. “a” indicated a suggestive p value (<0.05) for social network subcomponents in associations with CSF biomarkers of AD pathology. “b” indicated a significant p value (<0.008) for social network subcomponents in associations with CSF biomarkers of AD pathology after Bonferroni correction for 6 tests. Aβ, amyloid-β; T-tau, total tau; P-tau, phosphorylated tau, APOE ɛ4, apolipoprotein E ɛ4 allele; SCD, subjective cognitive decline.

The multivariable linear models next detected the associations of social network score as a comprehensive continuous variable with levels of CSF biomarkers (Fig. 1). The social network score was positively associated with CSF Aβ_1–42/Aβ_1–40 (β=0.1156, p = 0.01) and had negative links with levels of CSF T-tau/Aβ_1–42 (β= –0.0254, p = 0.04) and P-tau/Aβ_1–42 (β= –0.0115, p = 0.03) (Supplementary Table 4). To clarify whether some covariates could confound the associations between the social network and CSF biomarkers, we repeated another model after adding potential terms of CM-MMSE, living status, marital status, chronic diseases (including stroke, hypertension, and diabetes mellitus), physical exercise, and depression. Collectively, the correlations with CSF Aβ_1–42/Aβ_1–40 and CSF P-tau/Aβ_1–42 were persisted and robustness (Supplementary Table 4).

Modification effects of APOE ɛ4 and subjective cognitive decline

Next, we detected whether potential genetic factor and cognitive status could modify the aforementioned associations. We noted significant interactions of social network score with APOE ɛ4 carrier status or with cognitive status (SCD or controls) (Supplementary Table 5). Further stratified results supported the modification effects of genetic factor and cognitive status. However, there were nonsignificant interaction effects by sex, indicating the associations independent of sex difference (Supplementary Table 5).

In APOE ɛ4 carriers, the social network score was inversely associated with the level of CSF P-tau (β= –0.044, p = 0.007) (Supplementary Table 6 and Fig. 1). While the link with CSF Aβ_1–42/_1–40 in APOE ɛ4 non-carriers (β= 0.122, p = 0.01) did not reach a significant level after Bonferroni correction (p > 0.008). Similarly, individuals with higher social network scores showed higher CSF Aβ_1–42/_1–40 (β= 0.228, p < 0.001) and lower CSF P-tau/Aβ_1–42 (β= –0.021, p = 0.007) and CSF T-tau/Aβ_1–42 (β= –0.045, p = 0.009) (Supplementary Table 6 and Fig. 1) in the SCD group. No correlation was revealed in those without SCD (i.e., controls).

Social networks associated with CSF biomarkers of multiple pathologies in preclinical AD stage

We finally repeated association explorations in an early risk stage, namely preclinical AD, intending to pinpoint advantageous influences of social networks in a particular group. Some underlying correlations were more pronounced in individuals with preclinical AD in comparison with cognitively intact participants without biomarkers evidence of AD (i.e., controls).

Linear regression models adjusted for age, sex, years of education, and APOE ɛ4 status in the preclinical AD group showed associations between increased social network score and higher CSF Aβ_1–42/_1–40 (β= 0.481, p = 0.001) and lower CSF P-tau (β= – 0.0006, p = 0.04), CSF T-tau (β= –0.005, p < 0.001), CSF P-tau/Aβ_1–42 (β= –0.039, p = 0.03), and CSF T-tau/Aβ_1–42 (β= –0.047, p < 0.001) (Fig. 2 and Supplementary Table 7). As for control groups with or without neurodegeneration, no significant associations were revealed after adjustment for covariates, although the univariable results implied that individuals with higher social network scores showed higher CSF Aβ_1–42/_1–40 (β= 0.314, p = 0.02) in the control group with neurodegeneration (Fig. 2). All results were barely changed in the sensitivity analyses after adjustment for the potential confounders (Supplementary Table 8).

Fig. 2

The associations of social network score with CSF AD biomarkers in preclinical AD and controls. In preclinical stage of AD, a higher social network score was associated with lower CSF T-tau (A) and CSF T-tau/Aβ_1–42 (B), and higher CSF Aβ_1–42/Aβ_1–40 (C), while no such relationships were noted in control 1 and control 2 groups. The red line represents the fitted univariable linear relationship in preclinical stage of AD. The green line represents the fitted univariable linear relationship in controls without β-amyloidosis and neurodegeneration (controls 1). The black line shows the fitted linear relationship in controls with neurodegeneration in the absence of β-amyloidosis (controls 2). Covariates of adjusted models comprised age, sex, years of education, and APOE4 status. CSF, cerebrospinal fluid; Aβ, amyloid-β; T-tau, total tau.

DISCUSSION

In a large-scale sample of cognitively intact older adults, this cross-sectional study yielded the strong links of social networks with CSF biomarkers of AD pathology in total participants. These findings supported the close correlations between the social network and the incident AD and corroborated a definite role that social network might modulate not only the metabolism of amyloid protein but multiple core pathologies of AD in the preclinical stage. These associations were more pronounced in individuals with SCD and those at preclinical AD stage, which further strengthened the link between AD pathophysiology and social network. In addition, the stronger association between the social network and CSF P-tau in APOE ɛ4 carriers than in non-carriers indicated that genetic risk factor APOE ɛ4 could modify the association of social network to tau pathology.

A low social network score in the present study means relatively distant social ties and even social isolation, which may cause an increased prevalence of neurological diseases [39]. However, mechanisms mediating these processes are not completely examined, especially through clinical explorations on such relationships with AD pathology. CSF biomarkers and their ratios to Aβ have been deemed as the most intimately expressed biomarkers of the AD pathology [40]. The ratios of CSF biomarkers reflect different AD-related pathologies simultaneously during the preclinical timeframe, suggesting a higher risk for imminent disease [19]. In the present study, three ratios to beta-amyloid were mostly identified in associations with the social networks, which thus implies that the protective pathway of benign social networks may involve not only amyloid metabolism but mitigation of multiple AD pathologies. The researchers found social networks could also provide reserves of neural systems to prevent the brain from expressing the pathology of AD [41]. In line with our study, previous studies showed an elevated Aβ aggregation and an increased PS1 expression in isolated aged APP/PS1 mice, which was associated with increased γ-secretase and decreased neprilysin expression [42, 43].

In the present study, social-networks related factors such as living status and marital status were incorporated into linear models as adjusted variables because these factors could confound associations of social ties with CSF biomarkers, although shred of evidence considered them as subcomponents of social networks. Indeed, robust findings derived from sensitivity analyses saw that our correlations of interest were independent of both two factors (Supplementary Tables 4 and 8). And linear models showed that living and marital status did not associate with CSF AD biomarkers before we treated them as confounders (data not shown).

Stratified analyses presented several findings that differed from primary outcomes in the total participants. We found that the association of higher social network score with higher CSF P-tau was stronger in APOE ɛ4 carriers than non-carriers though no similar association for other biomarkers was revealed. In line with our study, a large body of evidence suggested that the APOE ɛ4 allele and social networks as reflected by cognitive reserve could interplay to affect the occurrence of mild cognitive impairment and AD [44 –46]. In addition, APOE ɛ4 allele could directly exacerbate tau pathogenesis independent of Aβ thereby contributing to AD [47]. It is thus rational to propose that social networks may attenuate the burden of AD pathology attributed to the APOE ɛ4 allele. Subjects with SCD in the absence of objective neuropsychological dysfunction are at elevated risks of cognitive decline and progression to AD dementia [24, 48]. As expected, some stronger associations of social network scores with CSF biomarkers were revealed in individuals with SCD than controls. Besides, the significant or suggestive associations of social network score with CSF biomarkers of AD pathology were only observed in individuals with preclinical AD, which indicates the hypothesis that evident relations may be observed in the population who have a trend for developing AD pathology.

Strengths of this study reinforce its implications for research. The findings based on cognitively intact individuals offer perspectives to hypothesis validation involving underlying mechanisms of social factors. In addition, large number of individuals with homogeneous data from Chinese Han cohort and representing the entire preclinical AD continuum provide credible conclusions. Besides, considering confounding and interaction, robust results of sensitivity analyses suggest that results are not driven by lifestyle relevant factors that influencing social activity. Another strength is our approach to reconstruct a social network score, reflecting more comprehensive social bonds than a single tie, by combining all items of social relationships. Noteworthily, the pronounced associations in SCD and preclinical AD groups underscore that social networks may decrease AD risks among at-risk populations (i.e., SCD and preclinical AD subjects) by mitigating loads of aggregated pathological proteins.

Limitations of this study include the application of semi-quantitative questionnaires and conclusions based on cross-sectional studies without necessary causality. When grouped moderate social contacts as the reference, we failed to find marked and significant associations of close social ties with CSF AD indicators (data not shown), which may be attributed to unavailable network size and contact frequencies between network members in our questionnaires. Therefore, the construction of quantitative and qualitative items in social network questionnaires would aid to test the potential dose-response relationships of social-networks subcomponents with alteration of AD pathologies, especially in multi-ethnicity backgrounds. In addition, future studies are thus warranted to detect longitudinal effects of social networks and other social factors against AD neuropathology combing CSF and neuroimaging measures.

To conclude, our study reports social network subcomponents and comprehensive social network index in the associations with alterations of CSF AD biomarkers during cognitive unimpaired stage and presents evidence for social networks as a modifiable lifestyle relevant to multiple core pathologies of AD, especially among at-risk populations. Following this research, we advanced the actions of strong social bonds indicated by social networks with close and mutual ties and discovered that social network properties might interfere with the silent accumulation of brain amyloidosis and neurodegenerative pathologies. This work will inform future research into the neurobiology of social networks and other socio-behavioral or socio-psychological factors, thereby promoting the intervention studies in AD.

Footnotes

AcknowledGMENTS

The authors thank all participants of the present study as well as all members of staff of the CABLE study for their role in data collection.

This work was supported by grants from the National Natural Science Foundation of China (91849126), the National Key R&D Program of China (2018YFC1314700), Taishan Scholars Program of Shandong Province (tsqn20161078), Shanghai Muni-cipal Science and Technology Major Project (No. 2018SHZDZX01), ZJLab, Shanghai Center for Brain Science and Brain-Inspired Technology, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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