Our study demonstrated that the integration of the neuropsychological measurements and blood-based markers significantly improved prediction accuracy. The prediction model has led to several simple rules, which have a great potential of being naturally translated into clinical settings such as enrichment screening for AD prevention trials of anti-amyloid treatments.

BackgroundThere is a need to investigate biomarkers that are indicative of the progression of dementia in ethnic patient populations. The disparity of information in these populations has been the focus of many clinical and academic centers, including ours, to contribute to a higher success rate in clinical trials. In this study, we have investigated plasma biomarkers in amnestic mild cognitively impaired (aMCI) female patient cohorts in the context of ethnicity and cognitive status.MethodA panel of 12 biomarkers involved in the progression of brain pathology, inflammation, and cardiovascular disorders were investigated in female cohorts of African American, Hispanic, and White aMCI patients. Both biochemical and algorithmic analyses were applied to correlate biomarker levels measured during the early stages of the disease for each ethnicity.ResultsWe report elevated plasma Aβ40, Aβ42, YKL-40, and cystatin C levels in the Hispanic cohort at early aMCI status. In addition, elevated plasma Aβ40 levels were associated with the aMCI status in both White and African American patient cohorts by the decision tree algorithm. Eotaxin-1 levels, as determined by the decision tree algorithm and biochemically measured total tau levels, were associated with the aMCI status in the African American cohort.ConclusionsOverall, our data displayed novel differences in the plasma biomarkers of the aMCI female cohorts where the plasma levels of several biomarkers distinguished between each ethnicity at an early aMCI stage. Identification of these plasma biomarkers encourages new areas of investigation among aMCI ethnic populations, including larger patient cohorts and longitudinal study designs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13195-016-0211-0) contains supplementary material, which is available to authorized users.

Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

Scientific Reports 6: Article number: 30828; published online: 26 August 2016; updated: 02 May 2017 In the original version of this Article, the author list for the “TEDDY Study Group” was incomplete. This has now been corrected in the PDF and HTML versions of this Article.