The Wisconsin Registry for Alzheimer’s Prevention (WRAP) began recruitment in 2001 as a prospective cohort study, with initial follow-up 4 years after baseline and subsequent ongoing follow-up every 2 years. WRAP is comprised of initially cognitively-unimpaired, asymptomatic, middle-aged (between 40 and 65) adults enriched for parental history of clinical AD (Johnson et al., 2018). At each visit, the participants undergo comprehensive medical and cognitive evaluations. Additional details of the study design and methods of WRAP have been described previously (Johnson et al., 2018). From the WRAP cohort, we identified 161 self-reported non-Hispanic white individuals with both longitudinal CSF biomarker and metabolomic data available from 2010 to 2017. The sample size for other racial/ethnic groups was too small (n < 10) to include in the analyses.

The Wisconsin Alzheimer’s Disease Research Center’s (ADRC) clinical core cohorts started in 2009 and are comprised of well-characterized participants who undergo cognitive testing and physical exams every 2 years (Bettcher et al., 2018). The Wisconsin ADRC has a cohort of initially cognitively-unimpaired, asymptomatic middle-aged (between 45 and 65) adults with a similar study design to WRAP [the Investigating Memory in Preclinical AD-Causes and Treatments (IMPACT) cohort] (Racine et al., 2016; Darst et al., 2017; Vogt et al., 2018). From the IMPACT cohort, we identified 154 self-reported non-Hispanic white participants with cross-sectional CSF biomarker and metabolomic data available between 2010 to 2017. As with WRAP, the sample size for other racial/ethnic groups was too small (n < 10) to include in the analyses.

This study was conducted with the approval of the University of Wisconsin Institutional Review Board, and all participants provided signed informed consent before participation.

Fasting CSF samples were collected via lumbar puncture using a Sprotte 25- or 24-gauge spinal needle at the L3/4 or L4/5 interspace with gentle extraction into polypropylene syringes. More details can be found in the previous study (Darst et al., 2017). The CSF collection for WRAP and the Wisconsin ADRC followed the same protocol, and the lumbar puncture for both studies was performed by the same group of well-trained individuals.

All CSF samples were batched together and assayed for the NTK biomarkers at the Clinical Neurochemistry Laboratory, University of Gothenburg, using the same lot of reagents, under strict quality control procedures. The immunoassays of Elecsys^® Aβ(1–42), P-tau(181P), and T-tau, as well as S100b and IL-6, were performed on a cobas e 601 analyzer (Hulle et al., 2021). The remaining NTK panel was assayed on a cobas e 411 analyzer including Aβ(1–40), α-synuclein, GFAP, YKL-40, sTREM2, NfL, and neurogranin (Hulle et al., 2021).

Cerebrospinal fluid metabolomic analyses and quantification were performed in one batch by Metabolon (Durham, NC) using an untargeted approach, based on Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry platform (UPLC-MS/MS) (Evans et al., 2014). Details of the metabolomic profiling regarding sample preparation, metabolite extraction, derivatization, separation and detection, and raw data processing were described in an earlier study (Darst et al., 2019).

A total of 412 CSF metabolites were identified and quality control procedures were performed. First, 46 metabolites missing for at least 80% of the individuals were excluded. Then the values for each of the remaining metabolites were scaled so that the median equaled 1. Two metabolites with an interquartile range (IQR) of zero were excluded and no metabolites had zero variability between individuals. Log10 transformation was applied to normalize the data. After quality control, 269 metabolites with known biochemical names remained for this investigation. The missing percentage of each metabolite in WRAP and Wisconsin ADRC is available in Supplementary Table 1.

In the WRAP participants, DNA was extracted from whole blood using the PUREGENE^® DNA Isolation Kit, and the concentrations were quantified using the Invitrogen™ Quant-iT™ PicoGreen™ dsDNA Assay Kit. More details can be found in the previous study (Darst et al., 2019). Genotyping data were generated by the University of Wisconsin Biotechnology Center using the Illumina Multi-Ethnic Genotyping Array for 1,340 individuals originally. Quality control procedures have been described previously (Darst et al., 2019). Briefly, samples and variants with missingness >5% and samples with inconsistent genetic and self-reported sex were removed. The resulting 1,198 samples from European ancestry individuals and 898,220 variants were then imputed using the Michigan Imputation Server and the HRC reference panel. Variants with a low imputation quality score (R2 < 0.8), with a low minor allele frequency (MAF) (<0.001), or out of HWE were removed. The genetic ancestry was assessed by using Principal Components Analysis in Related Samples (PC-AiR) because of the sibling relationships present in the WRAP cohort.

Genetic data in the Wisconsin ADRC were generated from DNA extracted from blood samples at baseline and genotyped with either the Infinium OmniExpressExome-8 Kit or the Infinium Global Screening Array-24 Kit. Genetic data for the Wisconsin ADRC underwent the same quality control (QC) and imputation as the WRAP data except samples and SNPs missing in >2% were excluded and HWE threshold was p < 1e-6 due to differences in sample sizes and the number of SNPs between the two cohorts.

In our analysis, we employed Mendelian randomization (MR) (Sanderson et al., 2022) which uses genetic variation as an instrumental variable to assess the causal relationship about whether metabolites influence the CSF NTK biomarkers. The genome-wide significant SNPs (p < 5 × 10^–8) from a previous genome-wide meta-analysis of CSF metabolites (Panyard et al., 2021) were extracted for each elastic net-selected metabolite (5,863 SNPs for 52 metabolites). These SNPs (or the top 100 SNPs if there were more than 100 genome-wide significant SNPs for a metabolite) were used as instrumental variables (IV) for the metabolite. Then we conducted one-sample MR analysis for each elastic net-selected metabolite-NTK biomarker association pair in the combined WRAP and Wisconsin ADRC cohort by using the summary statistics and individual-level genetic data of extracted SNPs. For each MR test, we first checked the strength of the IVs using F statistics. Typically, an IV with an F statistic greater than 10 is considered to be strong, while instruments with F statistics below 10 are considered to be weak (Stock et al., 2002). Next, the estimated (or less confounded) beta and p-values for the effect of the metabolite on the NTK biomarker were calculated using the two-stage least squares method if the IVs were strong and correlated, but using the limited information maximum likelihood (LIML) for correlated IVs that were relatively weak (Chao and Swanson, 2005; Wooldridge, 2010). The confidence intervals (CI) of the point estimates from both LIML and another conditional likelihood ratio (CLR) method, which is robust to weak IVs (Moreira, 2003), were compared and only significant results with CIs in the same direction and with a similar range of effect size between these two methods were considered as evidence of a causal effect. The Bonferroni corrected p-value < 0.05 based on the number of all MR tests performed was used to determine significance. The MR analysis was conducted using the R package “ivmodel” (Kang et al., 2021).

The genetic data have been uploaded to the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) and metabolomic data are being uploaded to Accelerating Medicines Partnership Program for Alzheimer’s Disease (AMP-AD).

Characteristics of the WRAP and Wisconsin ADRC participants can be found in Table 1. In the WRAP cohort, just over one third of the participants had one CSF sample, almost one third had two CSF samples collected approximately 2 years apart, and just over one third had three CSF samples collected approximately every 2 years. The first available measures were used to calculate the summary participant characteristics. In the Wisconsin ADRC cohort, only one sample was available for each participant. In both studies, the CSF was collected between 2010 and 2017. Among 161 WRAP participants, the mean age and education level were 62.1 and 16.2 years, respectively. The mean age and years of education in the Wisconsin ADRC were 58.1 and 16.2, respectively. Females comprised 65.2% of WRAP participants and 68.8% of the Wisconsin ADRC. In the longitudinal WRAP cohort, 80.1, 16.8, and 3.1% of participants were cognitively unimpaired stable, unimpaired declining, or had clinical MCI, respectively. Four participants progressed from cognitively unimpaired stable to unimpaired declining by the last CSF collection 2–4 years later. All participants in the Wisconsin ADRC were cognitively unimpaired stable. The mean values of each biomarker are also listed in Table 1.

The significant MWAS results in WRAP and the Wisconsin ADRC are summarized in Figure 1. In WRAP, a large number of CSF metabolites reached the significance threshold after FDR correction (Figure 1A). 47 metabolites were associated with P-tau, 56 were associated with T-tau, 58 were associated with Aβ42, 80 were associated with Aβ40, 65 were associated with NfL, and 62 were associated with neurogranin. However, no metabolites were associated with the ratio of Aβ42/40 or IL-6. Many of the metabolites that were significant in WRAP were also significant in the Wisconsin ADRC (Figure 1B). For example, among 47 significant metabolites for P-tau in WRAP, 40 metabolites were also significant in the Wisconsin ADRC. Table 2 shows the replication results for the top 10 significant CSF metabolite-biomarker associations (if there were 10 or more significant metabolites) in the Wisconsin ADRC. For example, the top three metabolites associated with P-tau and T-tau were 1-palmitoyl-2-stearoyl-GPC (16:0/18:0), N-acetylneuraminate, and C-glycosyltryptophan. N-acetylneuraminate and 1,2-dipalmitoyl-GPC (16:0/16:0) were the top two metabolites associated with Aβ42 and Aβ40. The top three metabolites associated with NfL were N-acetylthreonine, N-acetylalanine, and beta-citrylglutamate. N-acetylneuraminate, C-glycosyltryptophan, and N6-succinyladenosine were the top three metabolites for neurogranin. N-acetylneuraminate, 1,2-dipalmitoyl-GPC (16:0/16:0), and stearoyl sphingomyelin (d18:1/18:0) were the top three metabolites for YKL40. Stearoyl sphingomyelin (d18:1/18:0), 1-stearoyl-2-docosahexaenoyl-GPC (18:0/22:6), and 1-palmitoyl-2-oleoyl-GPC (16:0/18:1) were the top three metabolites associated with S100b. Only six metabolites were associated with GFAP, and the top three were 1,2-dipalmitoyl-GPC (16:0/16:0), 1-palmitoyl-2-stearoyl-GPC (16:0/18:0), and beta-citrylglutamate. For sTREM2, the top metabolites were stearoyl sphingomyelin (d18:1/18:0), 1,2-dipalmitoyl-GPC (16:0/16:0), and palmitoyl sphingomyelin (d18:1/16:0). Finally, for α-synuclein, the top three metabolites were 1-palmitoyl-2-stearoyl-GPC (16:0/18:0), 1,2-dipalmitoyl-GPC (16:0/16:0), and N-acetylneuraminate. The full results of WRAP and the Wisconsin ADRC can be found in Supplementary Tables 2–25. The association patterns between significant CSF metabolites and NTK biomarkers are provided in the Figure 2. The summary of the number of significant associations and the name of NTK biomarkers that were replicated in the Wisconsin ADRC are presented in Supplementary Table 26. Most of the significant metabolites were lipids, amino acids, and carbohydrates. For example, the lipid, 1,2-dipalmitoyl-GPC (16:0/16:0), the amino acid, beta-citrylglutamate, and the carbohydrate N-acetylneuraminate were strongly associated with almost every CSF NTK biomarker of AD. On the contrary, amino acids like kynurenate and proline were only significantly associated with α-synuclein.

The functional pathways for replicated significant metabolites with known human metabolome database (HMDB) IDs for each CSF NTK biomarker are shown in Supplementary Table 27. Two significant metabolites, 1,2-dipalmitoyl-GPC (16:0/16:0) and 1-oleoyl-GPC (18:1), were enriched in the glycerophospholipid metabolism pathway for most biomarkers. Other pathways such as pyrimidine metabolism (including orotate and orotidine), ascorbate and aldarate metabolism (including gulonate and glucuronate), arginine biosynthesis (including N-acetylglutamate and argininosuccinate), and pentose and glucuronate interconversions (also including gulonate and glucuronate) may also be of interest.

The prediction performance of replicated significant metabolites was measured by r² and presented in Table 3. The r² of the base models, which only included the demographic variables, ranged from 0.01 to 0.25. Adding the replicated significant metabolites increased the r² substantially for each biomarker, ranging from 0.23 to 0.74. The elastic net regression further prioritized candidate metabolites associated with each biomarker. For example, 14 of the original 40 significant metabolites were selected by the elastic net as important independent metabolites for P-tau. Initially, 40 significant metabolites explained about 72% of the variance in P-tau; the 22 elastic net-selected metabolites still explained 67% of the variance.

According to the F statistics, we employed the LIML method for MR. The full results of the test statistics are provided in Supplementary Table 28. After checking for consistency of the CIs for the LIML and CLR methods, the significant and consistent MR results are displayed in Table 4, showing metabolites with a potential causal effect on the NTK biomarker based on instrumental variables formed by genome-wide significant SNPs. For example, we observed a positive causal association between palmitoyl sphingomyelin (d18:1/16:0) and sTREM2.