Thrombopoietin is associated with δ's intercept, and only in Non-Hispanic Whites

Donald R Royall, Raymond F Palmer, Donald R Royall, Raymond F Palmer

Abstract

Introduction: Serum thrombopoietin (THPO) is a biomarker of Alzheimer's disease (AD) and the latent dementia phenotype, "δ". Both associations may be specific to non-Hispanic whites (NHW), not Mexican-Americans (MA). In this analysis, we examine ethnicity's effect on THPO's association with change in δ scores, in the Texas Alzheimer's Research and Care Consortium (TARCC).

Methods: We constructed an ethnicity equivalent δ homolog ("dEQ") among n = 1113 MA and n = 1958 NHW. dEQ was output as a composite "dEQ-score" for each of five annual TARCC waves. Those composites were used as indicators of a latent growth curve (LGC). The mean dEQ intercept (idEQ) and slope (ΔdEQ) were estimated in a random subset of N = 1528 participants and replicated in the remainder (n = 1544). THPO was regressed onto idEQ and ΔdEQ. Those associations were tested separately in MA and NHW.

Results: dEQ correlated strongly with CDR-SB (r = 0.99, P < .001) and achieved high AUCs for AD diagnosis at each wave (range = 0.95-0.99). THPO was significantly associated with idEQ but not ΔdEQ. That effect was observed in NHW only. In MA, THPO had no associations with either idEQ or ΔdEQ.

Discussion: We confirm THPO's ethnicity-specific association with δ in NHW. It is further clarified that this association is specific to δ's intercept and not its slope. This analysis provides a model for how dementia's specific serum biomarkers can be characterized.

Keywords: Aging; Cognition; Dementia; Functional status; Intelligence; THPO.

Figures

Supplementary Figure 1
Supplementary Figure 1
The latent variable dEQ's construction. Abbreviations: CFI, comparative fit index; COWA, Controlled Oral Word Association Test; DF, degrees of freedom; DST, Digit Span Test; IADL, instrumental activities of daily living; RMSEA, root mean square error of association; SE, standard error; WMS LM II, Weschler Memory Scale: delayed logical memory; WMS VR I, Weschler Memory Scale: immediate visual reproduction.
Supplementary Figure 2
Supplementary Figure 2
Observed serum THPO by diagnosis and ethnicity
Fig. 1
Fig. 1
dEQ histograms for selected waves (dEQ scores are referenced to the entire cohort's standardized mean [i.e., dEQ = 0.0]). NOTE. Rightward increase in dEQ scores.
Fig. 2
Fig. 2
THPO predicts dEQ's intercept but not its slope (group 1 loadings. Standardized parameter estimates. dEQ indicators are adjusted for age, gender, and education [but not ethnicity]).

References

    1. Royall D.R., Palmer R.F., O'Bryant S.E. Validation of a latent variable representing the dementing process. J Alzheimers Dis. 2012;30:639–649.
    1. Royall D.R. Welcome back to your future: The psychometric assessment of dementia by the latent variable “δ”. J Alzheimers Dis. 2016;49:515–519.
    1. Royall D.R., Palmer R.F., Matsuoka T., Kato Y., Taniguchi S., Ogawa M. Greater than the sum of its parts: δ can be constructed from item-level data. J Alzheimers Dis. 2016;49:571–579.
    1. Gavett B.E., John S.E., Gurnani A.S., Bussell C.A., Saurman J.L. The role of Alzheimer's and cerebrovascular pathology in mediating the effects of age, race, and apolipoprotein E genotype on dementia severity in pathologically confirmed Alzheimer's disease. J Alzheimers Dis. 2016;49:531–545.
    1. Koppara A., Wolfsgruber S., Kleineidam L., Schmidtke K., Frölich L., Kurz A. The latent dementia phenotype δ is associated with CSF biomarkers of Alzheimer Disease and predicts conversion to AD dementia in subjects with MCI. J Alzheimers Dis. 2016;49:547–560.
    1. Royall D.R., Palmer R.F. Ethnicity moderates dementia's biomarkers. J Alzheimers Dis. 2015;43:275–287.
    1. Gavett B.E., Vudy V., Jeffrey M., John S.E., Gurnani A.S., Adams J.W. The δ latent dementia phenotype in the uniform data set: cross-validation and extension. Neuropsychology. 2015;29:344–352.
    1. Palmer R.F., Royall D.R. Future dementia severity is almost entirely explained by the latent variable δ's intercept and slope. J Alzheimers Dis. 2016;49:521–529.
    1. O'Bryant S.E., Xiao G., Barber R., Huebinger R., Wilhelmsen K., Edwards M., Texas Alzheimer's Research & Care Consortium. Alzheimer's Disease Neuroimaging Initiative A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI. PLoS One. 2011;6:e28092.
    1. O'Bryant S.E., Xiao G., Zhang F., Edwards M., German D.C., Yin X. Validation of a serum screen for Alzheimer's disease across assay platforms, species, and tissues. J Alzheimers Dis. 2014;42:1325–1335.
    1. O'Bryant S.E., Xiao G., Edwards M., Devous M., Gupta V.B., Martins R. Biomarkers of Alzheimer's Disease among Mexican Americans. J Alzheimers Dis. 2013;34:841–849.
    1. Kissel K., Berber S., Nockher A., Santoso S., Bein G., Hackstein H. Human platelets target dendritic cell differentiation and production of proinflammatory cytokines. Transfusion. 2006;46:818–827.
    1. Wechsler D. 3rd ed. The Psychological Corporation; San Antonio, TX: 1997. Wechsler Memory Scale.
    1. Benton A., Hamsher K. AJA Associates; Iowa City, Iowa: 1989. Multilingual Aphasia Examination.
    1. Lawton M.P., Brody E.M. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9:179–186.
    1. Hughes C.P., Berg L., Danziger W.L., Coben L.A., Martin R.L. A new clinical scale for the staging of dementia. Br J Psychiatry. 1982;140:566–572.
    1. Waring S., O'Bryant S.E., Reisch J.S., Diaz-Arrastia R., Knebl J., Doody R., for the Texas Alzheimer's Research Consortium The Texas Alzheimer's Research Consortium longitudinal research cohort: Study design and baseline characteristics. Texas Public Health Journal. 2008;60:9–13.
    1. Stine R.A. Graphical interpretation of variance inflation factors. Am Stat. 1995;49:53–56.
    1. Mardia KV. Tests of univariate and multivariate normality. In Krishnaiah PR, ed. Handbook of Statistics, Volume 1; 1980. p. 279–320. Chapter 9, North-Holland, Amsterdam.
    1. Arbuckle J.L. SPSS; Chicago: 2006. AMOS 18 User's Guide.
    1. Grice J.W. Computing and evaluation factor scores. Psychol Methods. 2001;6:430–450.
    1. Graham J.W. Missing data analysis: Making it work in the real world. Annu Rev Psychol. 2009;6:549–576.
    1. Enders C.K., Bandalos D.L. The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Struct Equ Modeling. 2001;8:430–457.
    1. Newman D.A. Missing data techniques and low response rates: The role of systematic nonresponse parameters. In: Lance C.E., Vandenberg R.J., editors. Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences. Routledge/Taylor & Francis Group; New York, NY, US: 2009. pp. 7–36. xix.
    1. PASW Statistics 18, Release Version 18.0.0. SPSS, Inc; Chicago, IL: 2009.
    1. Guttman L. The determinancy of factor score matrices with applications for five other problems of common factor theory. Br J Stat Psychol. 1955;8:65–82.
    1. Bollen K.A., Long J.S. Sage Publications; Thousand Oaks, CA: 1993. Testing structural equation models.
    1. Wheaton B., Muthén B., Alwin D.F., Summer G.F. Assessing reliability and stability in panel models. In: Heise D.R., editor. Sociology Methodology. Jossey-Bass; San Francisco, CA: 1977.
    1. Bentler P.M. Comparative fit indexes in structural models. Psychol Bull. 1990;107:238–246.
    1. Browne M., Cudeck R. Alternative ways of assessing model fit. In: Bollen K.A., Long J.S., editors. Testing structural equation models. Sage Publications; Thousand Oaks, CA: 1993. pp. 136–162.
    1. Zweig M.H., Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–577.
    1. Mapstone M., Cheema A.K., Fiandaca M.S., Zhong X., Mhyre T.R., MacArthur L.H. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med. 2014;20:415–418.
    1. Reiff R.E., Ali B.R., Baron B., Yu T.W., Ben-Salem S., Coulter M.E. METTL23, a transcriptional partner of GABPA, is essential for human cognition. Hum Mol Genet. 2014;23:3456–3466.
    1. Witte M.M., Trzepacz P., Case M., Yu P., Hochstetler H., Quinlivan M. Association between clinical measures and Florbetapir F18 PET neuroimaging in mild or moderate Alzheimer's disease dementia. J Neuropsychiatry Clin Neurosci. 2014;26:214–220.
    1. Kwon O.D., Khaleeq A., Chan W., Pavlik V.N., Doody R.S. Apolipoprotein E polymorphism and age at onset of Alzheimer's disease in a quadriethnic sample. Dement Geriatr Cogn Disord. 2010;30:486–491.
    1. Janicki S.C., Park N., Cheng R., Clark L.N., Lee J.H., Schupf N. Estrogen receptor α variants affect age at onset of Alzheimer's disease in a multiethnic female cohort. Dement Geriatr Cogn Disord. 2014;38:200–213.

Source: PubMed

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