dataesr/scientific-paragraphs-categorization huggingface dataset:
15 SD, IVW MRE p = 3. 33E-12). In the analysis using 2 proxy SNPs inside the APOE region, 4 of the 8 proteins remained significant (CRP, CTSZ, FAM177A1, and UBE2G2), and none were significant using 15 proxy SNPs outside of the APOE region (IVW MRE p > 1. 6E-3, Supplementary Data 35). Using both clinically diagnosed AD and AD-by-proxy as the exposure (Jansen et al. dataset)  20  , we identified 8 proteins as potentially affected by AD susceptibility (Supplementary Data 38, Supplementary Figs. 24-31). All these results were also supported by one or more methods robust to pleiotropy at p < 0. 05 (Supplementary Data 39). In individual-level data, these protein measures were modestly correlated (abs(Pearson correlation of log2 transformed values, median [1st and 3rd quartile]: 0. 17 [0. 11, 0. 23], Supplementary Data 37).
Of these 8 significant proteins, two (CTSZ and FAM177A1) were the same as those identified using clinically diagnosed AD as exposure (Kunkle et al. stage 1 dataset) with the same effect direction  19. In the analysis using proxy SNPs inside of the APOE region, 4 of the 8 proteins remained significant, and none were significant using proxy SNPs outside of the APOE region (IVW MRE p > 8. 69E-2, Supplementary Data 38). The proxy SNPs of AD had modest heterogeneity in the pQTL meta-analysis (I2, median (1st, 3rd quartile): 0 (0, 16. 4), Supplementary Data 30). Interestingly, among the 14 unique protein measures that were significantly affected by AD susceptibility using the Kunkle et al. or Jansen et al. datasets, the effects from the MR analysis were in an unexpected direction from their associations with cognitive function in the discovery analysis (Supplementary Data 40).
Some limitations warrant mentioning. Two of the discovery cohorts (CHS and FHS Gen 3) had a time gap between blood drawn for protein assay and cognitive assessment. However, there was a high level of consistency in the association results between CHS and ARIC, which did not have a time gap between blood drawn for protein assay and cognitive assessment. Among the protein measures that were significant in the discovery analysis, some had considerable heterogeneity, which may partly reflect different protein levels between middle age and older age  55. We also had limited sample size for replication. These limitations represent the challenges in the current state of proteomics studies, including heterogeneity between assay platforms and protein levels in subpopulations  8, 55. In two-sample Mendelian randomization analysis, the results might be biased toward false negative when the datasets for the exposure and the outcome did not overlap, such as in our case  56.
However, given that we used strong genetic proxies (SNPs with genome-wide significance), the bias is likely small  56. For the MR analysis using <3 proxy SNPs, there is a lack of methods for evaluating potential pleiotropy. Finally, the MR estimates based on the datasets that included AD by proxy as cases may be biased and did not adequately represent AD risk. In conclusion, we identified circulating proteins associated with cognitive function across several independent cohorts, implicated higher NECTIN2 levels as increasing AD risk, and the potential role of AD susceptibility from the APOE region in regulating some circulating protein levels.
Methods Study population in discovery and replication analyses. The participant inclusion criteria were aged ≥25, without prevalent dementia and stroke, and having data for proteomics, cognitive function, and covariates. The discovery analysis included three population-based cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium: the Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), and Framingham Heart Study (FHS) Gen 3  57, 58. These discovery cohorts contributed protein measures quantified using the SomaScan assays from SomaLogic (Boulder, CO) (Supplementary Note 2). The primary replication analysis included four cohorts using protein measures quantified using the SomaScan assay (the Age, Gene   2 ). This study was conducted in accordance with the Declaration of Helsinki.
All participants gave their informed consent for inclusion before they participated in the studies. The protocol of the studies have been approved by the respective institutional review board. Quantification of circulating proteins. Technical variations of the protein assays were normalized based on manufacturer protocols (Supplementary Data 2). For protein measures quantified using the SomaScan platform, non-human proteins and proteins that were known to bind to contaminates or related proteins with similar affinity were excluded from the discovery meta-analysis (Supplementary Data 2). Outcome definitions. The primary outcome was general cognitive function, represented by the first unrotated principal component (PC1) of cognitive scores from at least three different domains (Supplementary Data 6).
The principal component scores were oriented in the direction of higher cognitive function. This approach has been used by previous GWAS for discovering associations between genetic variants and general cognitive function  3. The variance of the cognitive test scores by PC1 ranged from 0. 47 to 0. 67 among discovery and replication cohorts (Supplementary Data 7). The contribution of each cognitive score to PC1 (loading) and the correlation between each cognitive score and PC1 are reported in Supplementary Data 5 and 6. We also used the score from the DSST, a test assessing processing speed, as a separate outcome. The DSST was used by two discovery cohorts and was included in the generation of general cognitive function in these cohorts (Supplementary Data 5).
Multicenter and multi-ethnic cohorts also included center and selfreported race as covariates. The overall analysis included all participants aged ≥25 in the 3 discovery cohorts (n = 7289). An association analysis only including participants aged ≥65 (n = 6583) was also conducted given that the levels of some circulating proteins have large variations from middle age to older age and the association between circulating proteins and cognitive function may differ by age group  8, 55. This analysis only included data from the two discovery cohorts (ARIC and CHS) with participants aged ≥65. In the discovery meta-analysis, we used the inverse variance weighted fixed effects method to combine the association statistics. Given that the meta-analysis of general cognitive function among those aged ≥25 only had one cohort with participants aged <65 and the meta-analysis among those age ≥65 only had two cohorts, we required that each meta-analysis included only protein measures that were present in all cohorts.
Exploration of relationships between proteins replicated on the SomaScan platform. To explore potential relationships among protein measures replicated at Bonferroni-corrected significance using the SomaScan platform, we plotted the pairwise Pearson correlations between these measures in each analysis after applying hierarchical clustering based on Euclidean distance of the correlations using the R package heatmaply version 0. 14. 1  62. We also plotted the potential interactions between the replicated proteins in each analysis using data from the STRING database (version 12)  15. Sensitivity and other analyses of cognition-associated proteins. For the protein measures that were significant in the discovery study, we interrogated a large-scale GWAS of general cognitive function for genetic variants that were associated at p < 5E-8 and located at 500 kb on both sides of the promoter of the encoding gene  3.
05  76. When a protein measure was the exposure, to reduce potential confounding and pleiotropy, the genetic proxies for protein measures were limited to those located within 500 kb of the promoter of the encoding gene of the protein  77. We excluded measures annotated to a pseudogene (BAGE2), which did not have promoter information, and protein complexes (C5/C6, CGA/FSHB, and CGA/LHB) because the promoter regions of these encoding genes were hard to define. In addition, genetic proxies for protein measures were required to exist in at least two of the three pQTL datasets to ensure that the association between the genetic proxy and the protein measure was not driven by only one pQTL dataset. For the primary analysis methods, when a protein measure had only one genetic proxy, we used Wald ratio.
In addition, given that some SNPs that are strongly associated with eGFR may be proxy of serum creatinine instead of kidney function, we followed published methods to use the association of the SNP with blood urea nitrogen (BUN) to filter out SNPs that likely represented association with serum creatinine rather than kidney function  [84] [85] [86]. We requires that a proxy SNP of eGFR was also associated with BUN in opposite effect direction (higher kidney function, lower BUN levels) at p-value < 0. 05. GWAS datasets used in two-sample Mendelian randomization (MR) analyses. The GWAS summary statistics of general cognitive function were from a meta-analysis of the summary statistics from the Cognitive Genomics Consortium (COGENT) and the UK Biobank (n = 265,014)  3  and did not include cohorts in the protein discovery analysis of cognitive function.
The study populations of these cognition-related outcomes were also of European ancestry. The populations of the pQTL summary statistics and those of the cognition-related summary statistics did not overlap. The primary GWAS summary statistics of AD were from the stage 1 meta-analysis of Kunkle et al. (n = 21,982 clinically diagnosed late-onset AD cases, 41,944 cognitively normal controls)  19. To assess the generalizability of the primary MR analysis of protein on AD susceptibility, significant MR findings of proteins on AD using the Kunkle et    20, 74. The Jansen et al. dataset was also used to identify proteins whose levels might be affected by AD susceptibility. The eGFR GWAS summary statistics were the results from Stanzick et al. 2021 (European ancestry n = 1,004,040)  86. The BUN summary statistics were from Wuttke et al.
Since there may be potentially multiple causal variants at a locus, we also used Sum of Single Effects (SuSiE) analysis  90, 91  , which applied variable selection method in regression to identify independent signals in the association of the SNPs with the exposure and outcome separately. Then for each pair of independent signals from the two traits, SuSiE applied colocalization analysis to estimate the posterior probability of having a shared causal variant (PP-H 4 ). The reference panel used for the SuSiE analysis was 1000 Genomes EUR. We performed colocalization analyses using the coloc R package and used a PP-H 4 > 80% to conclude that both traits shared the same causal variant. Query of the association of proxy SNPs of protein measures with gene expression. For protein measures with significant effects on AD susceptibility or general cognitive function from MR analysis, we queried the associations of their proxy SNPs with gene expression in two datasets to determine whether these proxy SNPs might be associated with the expression of the proteinencoding gene.
The first dataset was the tissue-specific expression of cis-genes in the GTEx project (version 8)  17. The second dataset consisted of cortex-specific gene expression data  18. For proxy SNPs of protein measures that were located near APOE, we looked up the linkage disequilibrium of the proxy SNP with the SNPs that form the APOE4 haplotype (rs429358 and rs7412). Additional analysis on the proteins potentially affected by AD susceptibility. To explore the relationship between AD susceptibility from the APOE region and the protein measures that were significant in the MR analysis, we used linear regression to evaluate the association of APOE ε4 carrier status as the independent variable and each protein measure as the outcome controlling for age, sex, education attainment, race-center in ARIC and CHS followed by using fixed effect inverse variance weighted method to combine the results.
Acknowledgements The  ARIC study  has been funded in whole or in part with Federal funds from the  National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services  (contract numbers  HHSN268201700001I ,  HHSN268201700002I ,  HHSN268201700003I ,  HHSN268201700004I  and  HHSN268201700005I ),  R01HL087641 ,  R01HL059367  and  R01HL086694 ;  National Human Genome Research Institute  contract  U01HG004402 ; and  National Institutes of Health  contract  HHSN268200625226C. Funding was also supported by  5RC2HL102419 ,  R01NS087541  and  R01HL131136. Neurocognitive data were collected by  U01 2U01HL096812 ,  2U01HL096814 ,  2U01HL096899 ,  2U01HL096902 ,  2U01HL096917  from the  NIH  ( NHLBI ,  NINDS ,  NIA  and  NIDCD). Infrastructure was partly supported by Grant Number  UL1RR025005 , a component of the  National Institutes of Health  and  NIH Roadmap for Medical Research.
AEF is supported by  K01AG071689. The  Framingham Heart Study  is conducted and supported by the  National Heart, Lung, and Blood Institute (NHLBI)  in collaboration with  Boston University  (Contract No. N01-HC-25195 ,  HHSN268201500001I  and  75N92019D00031 ). This work was also supported by grant  R01AG063507 ,  R01AG054076 ,  R01AG049607 ,  R01AG059421 ,  R01AG033040 ,  R01AG066524 ,  P30AG066546 ,  U01 AG052409 ,  U01 AG058589  from from the  National Institute on Aging  and  R01 AG017950 ,  UH2/3 NS100605 ,  UF1 NS125513  from  National Institute of Neurological Disorders and Stroke  and  R01HL132320. AGES has been funded by  NIA  contracts  N01-AG012100  and  HSSN271201200022C ,  NIH  Grant No. 1R01AG065596-01A1 ,  Hjartavernd  (the  Icelandic Heart Association) , and the  Althingi  (the  Icelandic Parliament).
M. R. Duggan ,  T. Tanaka ,  J. Candia ,  K. A. Walker ,  L. Ferrucci ,  L. J. Launer ,  O. Meirelles  are funded by the  National Institute on Aging Intramural Research Program. This study was funded, in part, by the  National Institute on Aging Intramural Research Program. We thank the BLSA participants and staff for their participation and continued dedication. The  Coronary Artery Risk Development in Young Adults Study (CARDIA ) is supported by contracts  HHSN268201800003I ,  HHSN268201800004I ,  HHSN268201800005I ,  HHSN268201800006I , and  HHSN268201800007I  from the  National Heart, Lung, and Blood Institute (NHLBI). The  LBC1921  was supported by the  UK's Biotechnology and Biological Sciences Research Council (BBSRC) ,  The Royal Society , and The  Chief Scientist Office of the Scottish Government.
Genotyping was funded by the  BBSRC  ( BB/F019394/1 ). LBC1936  is supported by the  Biotechnology and Biological Sciences Research Council , and the  Economic and Social Research Council  [ BB/W008793/1 ],  Age UK  ( Disconnected Mind project ), and the  University of Edinburgh. Genotyping was funded by the  BBSRC  ( BB/F019394/1 ). The Olink® Neurology Proteomics assay was supported by a  National Institutes of Health (NIH)  research grant  R01AG054628. Phenotype harmonization, data management, sample-identity QC, and general study coordination of the MESA were provided by the  TOPMed Data Coordinating Center  ( 3R01HL-120393-02S1 ), and  TOPMed MESA Multi-Omics  ( HHSN2682015000031/HSN26800004 ). The MESA projects are conducted and supported by the  National Heart, Lung, and Blood Institute (NHLBI)  in collaboration with MESA investigators.
Support for the  Multi-Ethnic Study of Atherosclerosis  (MESA) projects are conducted and supported by the  National Heart, Lung, and Blood Institute (NHLBI)  in collaboration with MESA investigators. Support for MESA is provided by contracts  75N92020D00001 ,  HHSN268201500003I ,  N01-HC-95159 ,  75N92020D00005 ,  N01-HC-95160 ,  75N92020D00002 ,  N01-HC-95161 ,  75N92020D00003 ,  N01-HC-95162 ,  75N92020D00006 ,  N01-HC- 95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1TR001881, DK063491, and R01HL105756. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. The Rhineland Study is funded by the German Center for Neurodegenerative Disease (DZNE).
This work was further partly supported by the German Research Foundation (DFG) under Germany's Excellence Strategy (EXC2151-390873048) and SFB1454 -project number 432325352; the Federal Ministry of Education and Research under the Diet-Body-Brain Competence Cluster in Nutrition Research (grant numbers 01EA1410C and 01EA1809C) and in the framework "PreBeDem -Mit Prävention und Behandlung gegen Demenz" (grant numbers 01KX2230); and the Helmholtz Association under the Initiative and Networking Fund (grant number RA-285/ 19) and the 2023 Innovation Pool. The 3 C Study is supported by a grant overseen by the French National Research Agency (ANR) as part of the "Investment for the Future Programme" ANR-18-RHUS-0002. It has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 667375.
Data availability The summary statistics of the discovery and replication meta-analysis of the association between circulating protein and cognitive function, including the sensitivity analysis controlling for kidney function, are available in figshare (https://doi. org/10. 6084/m9. figshare. 24069354). Source data underlying Fig. 3  are reported in Supplementary Data 9, 15, and 21. The datasets used in Mendelian randomization were download from the studies that published those results: deCODE protein quantitative trait loci (pQTL) dataset https://www. decode. com/summarydata/, Interval pQTL dataset: the European Genotype Archive (accession number EGAS00001002555), the Fenland pQTL dataset: https://gwas. mrcieu. ac. uk/ and https://www. ebi. ac. uk/gwas/, the GWAS of Alzheimer's disease by Kunkle et  al.
: The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) accession NG00075, the GWAS of Alzheimer's disease by Jansen et al. : https://ctg. cncr. nl/software/summary_statistics. The gene expression quantitative trait loci (eQTL) data were download from the Genotype-Tissue Expression (GTEx) portal https://gtexportal. org/home/datasets, the GWAS of general cognitive function: requests can be sent to the chairs of the CHARGE and COGENT consortia.
al. dataset were further analyzed in two additional sets of summary statistics: the discovery summary statistics of Bellenguez et al. (n = 20,464 clinically diagnosed late-onset AD cases and 22,244 cognitively normal controls) which excluded the APOE region, and Phase 3 analysis of Jansen et al. (n = 359,856), whose cases overlapped with the Kunkle et al. dataset and included participants with clinically diagnosed late-onset AD and those having a parent with AD (AD-by-proxy cases).
In the current study, we used comparable behavioural data on face-to-face early interactions in rhesus macaques and compared them to the human data previously collected. Specifically, by using focal animal sampling in an outdoor setting, we video-recorded macaque mother-infant interactions occurring from the infant's day of birth to 2 weeks. The ages chosen in monkeys are comparable to those in humans, given that development is approximately four time faster in macaques than in humans  [49] [50] [51] [52] [53] [54]  (i. e. , a 2 day-old rhesus is comparable to a 8 day-old human, and a 2 week-old rhesus is comparable to a 2-month-old human), and that rhesus macaque mother-infant communicative exchanges are more frequent during the first two weeks of infant life, and significantly decrease after that time  39.
Therefore, in future studies, spontaneous human mother-infant face-to-face interactions during home observations, similar to those coded in macaques in this study, should be collected in order to provide more consistent data on the development and structure of these interactions and increase cross-species comparability. Finally, longitudinal observations combining a developmental, cross-cultural and cross-species perspective should be conducted in order to investigate the role of specific maternal responses in later infant development. In particular, future studies exploring the role of maternal responsiveness to infant vocal signals in different primate species could provide important information on the evolution of language and social communication.
Procedure. Human subjects. In home visits at 1, 3, 5, 7 and 9 weeks postpartum, 3 min of mother-infant face-to-face interaction were video-recorded. Infants were placed semi-reclined on a mat on the floor, and mothers sat opposite, leaning towards their infant. When the infants were calm and alert, and ready to engage, mothers were asked to interact with their infants as they would normally do. A camera filmed the infant's face and upper body, and a reflection of the mother's face and upper body in a mirror placed behind the infant; another mirror placed alongside the infant showed their face if they turned away from the camera. A total of 83 episodes of face-to-face interactions was observed and coded. Rhesus macaques. Behavioural data were collected on mother-infant dyads housed in indoor-outdoor enclosures in social groups (one adult male, several females and offspring).
Data analysis. For each sample, human and macaque, infant events and maternal responses were investigated through Principal Components Analysis (PCA), with Simplimax rotation, and using Parallel Analysis, to determine the number of components to extract. We used a generalised linear mixed modelling (GLMM) framework to address the study questions. Given the count nature of behavioural variables, two-level random intercept Poisson models were used to analyse the effect of infant age, group, and their interaction on infant and maternal behaviours, using interaction as offset, and a dataset including each infant at each age. Three-level random intercept Binomial models were used to compare groups in terms of how mothers distributed their responses across the different infant behaviours, using responded infant behaviours as cases (in a binary form of target behaviour vs.
Acknowledgements This research was supported by the  Newton International Fellowship   H5199800 , the  Division of Intramural Research ,  NICHD , and by  NIH   PO1HD064653  grant. We thank  Kyla Vaillancourt  and  Louise Dalton  for assistance with ethics submission and data collection for the human sample and the families who participated. We thank  Dr Stephen Suomi , Senior Research Associate at the  Laboratory of Comparative Ethology.
1 | Mapping The land topography data covering the study area are derived from a DEMNAS Digital Elevation Model (http://tides. big. go. id/DEMNAS) with a spatial resolution of 0. 27 arc-sec of longitude and latitude and referenced to the Earth Gravitational Model 2008 for the vertical datum. The spatial and vertical resolutions of the DEM are 8 and 1. 8 m, respectively. The DEM combines elevation data from IFSAR/ TERRASAR-X and ALOS PALSAR, with spatial resolutions of 5 and 11. 25 m, respectively, in addition to mass point data resulting from stereo-plotting. For the ocean bathymetry, we used BATNAS data (https://tanahair. indonesia. go. id/demnas/#/batnas). These bathymetric data were acquired within an offshore survey from the Center for Marine and Coastal Environment, the Geospatial Information Agency of the Republic of Indonesia.