CSF sphingomyelin metabolites in Alzheimer's disease, neurodegeneration, and neuroinflammation. (31st December 2021) - Morrow, Autumn Rose; Panyard, Daniel J.; Deming, Yuetiva; Dong, Ruocheng; Vasiljevic, Eva; Betthauser, Tobey J.; Jonaitis, Erin M.; Kollmorgen, Gwendlyn; Suridjan, Ivonne; Zetterberg, Henrik; Blennow, Kaj; Hulle, Carol A.; et al.
Abstract: Background: Several Alzheimer's disease (AD) metabolomics studies in cerebrospinal fluid (CSF) and plasma identified associations between sphingomyelin metabolites (SMs) and AD. However, the results were inconsistent, potentially due to small sample sizes. They were limited in the AD, neurodegeneration, and neuroinflammation biomarkers available for additional analyses. Leveraging two longitudinal preclinical and clinical AD cohorts with CSF metabolomics data, we analyzed the relationship between CSF SMs and biomarkers and measures of AD (PET Pittsburgh Compound B [PiB], amyloid‐β42/40, phosphorylated‐tau181, phosphorylated‐tau181 /amyloid‐β42), neurodegeneration (neurofilament light [NFL], neurogranin, α‐synuclein), and neuroinflammation (soluble triggering receptor expressed on myeloid cells 2 [sTREM2], interleukin‐6 [IL‐6], chitinase‐3‐like protein 1 [YKL‐40]). Method: We used data from the Wisconsin Registry for Alzheimer's Prevention (WRAP) and the Wisconsin Alzheimer's Disease Research Center (ADRC). Participants were aged 45‐91 years with available CSF metabolomics and biomarker data (n = 493 individuals [726 visits]; 44 individuals had diagnosed AD, 40 had mild cognitive impairment [MCI], and 409 were cognitively unimpaired). CSF biomarkers were measured with the Roche NeuroToolKit assays, a panel of automated robust prototype immunoassays. Associations between 12 SMs and MCI and AD diagnoses were analyzed using logistic regressions. Cross‐sectional global amyloid burden in the brain, measured by PiB‐PET, was regressed on the SMs using linear regression models. The nine other longitudinal CSF biomarkers for AD, neurodegeneration, and neuroinflammation were regressed on SMs using linear mixed models. All models were adjusted for sex and age. A Bonferroni correction (p<3.47×10 ‐4 ) was used to determine statistical significance. Result: No SMs were significantly associated with MCI or AD diagnosis (Table 1) or amyloid markers (Table 2). Phosphorylated‐tau181 was significantly, positively associated with six SMs. NFL and neurogranin were significantly, positively associated with 11 SMs. α‐synuclein was significantly, positively associated with 6 SMs (Table 3). sTREM2 and YKL40 were significantly, positively associated with 9 and 5 SMs, respectively (Table 4). Stearoyl SM had the strongest association for each set of SM‐biomarker regressions. Conclusion: Associations between SMs and biomarkers of neurodegeneration and neuroinflammation, but not markers of amyloid or diagnoses of MCI or AD implicate SMs as potential biomarkers for neurodegeneration and neuroinflammation that may not be AD‐specific.