δ Scores are Exportable Across Cultural and Linguistic Boundaries

A δ Homolog for Dementia Case Finding with Replication in the Alzheimer's Disease Neuroimaging Initiative

Dementia can be empirically described by the latent dementia phenotype "δ" and its various composite "homologs". We have explored δ's blood-based protein biomarkers in the Texas Alzheimer's Research and Care Consortium (TARCC) study. However, it would be convenient to replicate those associations in the Alzheimer's Disease Neuroimaging Initiative (ADNI). To this end, we have engineered a δ homolog from observed cognitive performance measures common to both projects. Our findings were replicated in randomly selected 50% splits of TARCC data (Group 1, N = 1,747; Group 2, N = 1,755), and then independently in ADNI (N = 1,737). The new δ homolog, i.e., "dT2A" (d-TARCC to ADNI), fit the data of both studies well, and was strongly correlated with dementia severity, as rated by the Clinical Dementia Rating Scale "sum of boxes" (TARCC: r = 0.99, p < 0.001; ADNI: r = 0.96, p < 0.001). dT2A achieved an area under the receiver operating characteristic curve of 0.981 (0.976-0.985) for the discrimination of Alzheimer's disease from normal controls in TARCC, and 0.988 (0.983-0.993) in ADNI. dT2A is the 12th δ homolog published to date, and opens the door to independent replications across these and similar studies.

δ Scores Identify Subsets of "Mild Cognitive Impairment" with Variable Conversion Risks

BACKGROUND

The latent variable "δ" (for "dementia) is a transdiagnostic measure of dementia severity. δ can be reified and applied to individuals as a composite "d-score". Like Spearman's general intelligence factor "g", δ can be constructed from almost any cognitive battery. So many are available that we must further distinguish each composite as a δ "homolog". Fourteen have been validated. All are strongly associated with dementia severity and potentially with mild cognitive impairment (MCI) conversion.

OBJECTIVES

To assess δ's impact on MCI conversion risk.

METHODS

A new δ homolog (dDx) was constructed in 1,230 Mexican-American (MA) and 2,215 non-Hispanic White (NHW) participants in the Texas Alzheimer's Research and Care Consortium (TARCC). 1,445 normal controls (NC) and 723 MCI were followed annually for up to 6 years.

RESULTS

Each SD decrease in the dDx score increased the risk of conversion sixteen-fold [OR = 16.39 (CI: 5.0-52.6)]. Cases below the optimal diagnostic threshold for Alzheimer's disease (AD) versus NC were labeled as having a functionally salient cognitive impairment (FSCI). Such cases were at a 73-fold increase risk of a diagnosis of AD [OR = 73.19 (95% CI: 58.3-92.0)]. However, 25.6% of MCI cases were also FSCI(+). They accounted disproportionately for prospective conversions. Age <80 years, the absence of an ɛ4 allele, <12 years of education, and MA ethnicity independently increased the risk of diagnosing FSCI as MCI.

CONCLUSION

A sizable minority of MCI cases may be misdiagnosed and they account disproportionately for AD conversions.

Exportation and Validation of Latent Constructs for Dementia Case Finding in a Mexican American Population-based Cohort

BACKGROUND

The latent variable "δ" has been validated as a dementia phenotype. δ can be extracted from Spearman's general intelligence factor "g" in any data set that contains measures of cognition and instrumental activities of daily living (IADL). We used δ composites ("d-scores") to estimate the prevalence of dementia in the Hispanic Established Population for Epidemiological Studies in the Elderly (H-EPESE).

METHOD

δ was constructed from Mini-Mental State Examination, a clock-drawing task (CLOX), and IADL. δ's H-EPESE factor weights were validated in the well-characterized Texas Alzheimer's Research and Care Consortium (TARCC). Optimal thresholds for the discrimination between "Alzheimer's disease" (AD) versus normal controls (NCs) were determined by receiver operating characteristic curve. Those thresholds were used to estimate the prevalence of dementia in H-EPESE.

RESULTS

Each δ homolog fits its source's data well. d-scores were strongly associated with Clinical Dementia Rating scale Sum of Boxes (r = .74-.85, all p < .001], and accurately distinguished AD cases from NCs, in both Mexican Americans (MAs) and non-Hispanic Whites (NHWs) [c = 0.94-0.96]. The TARCC MA threshold estimated the prevalence of dementia at 21.4% in H-EPESE. The NHW threshold estimated the prevalence of dementia at 21.0%.

CONCLUSIONS

It is possible to export δ composites from populations to well-characterized cohorts for validation.

Serum proteins mediate depression's association with dementia

The latent variable "δ" (for "dementia") uniquely explains dementia severity. Depressive symptoms are independent predictors of δ. We explored 115 serum proteins as potential causal mediators of the effect of depressive symptoms on δ in a large, ethnically diverse, longitudinal cohort. All models were adjusted for age, apolipoprotein E, education, ethnicity, gender, hemoglobin A1c, and homocysteine, and replicated in randomly selected 50% subsets. Alpha1-antitrypsin (A1AT), FAS, Heparin-binding EGF-like Growth Factor (HB-EGF), Insulin-like Growth Factor-1 (IGF-1), Luteinizing Hormone (LH), Macrophage Inflammatory Protein type 1 alpha (MIP-1α), Resitin, S100b, Tissue Inhibitor of Metalloproteinase type 1 (TIMP-1), and Vascular Cell Adhesion Molecule type 1 (VCAM-1) each were partial mediators of depression's association with δ. These proteins may offer targets for the treatment of depression's specific effect on dementia severity and Alzheimer's Disease (AD) conversion risk.

Validation of a Latent Construct for Dementia in a Population-Wide Dataset from Singapore

BACKGROUND

The latent variable δ has been proposed as a proxy for dementia. Previous validation studies have been conducted using convenience samples. It is currently unknown how δ performs in population-wide data.

OBJECTIVE

To validate δ in Singapore using population-wide epidemiological study data on persons aged 60 and above.

METHODS

δ was constructed using items from the Community Screening Instrument for Dementia (CSI'D) and World Health Organization Disability Assessment Schedule (WHODAS II). Confirmatory factor analysis (CFA) was conducted to examine δ model fit. Convergent validity was examined with the Clinical Dementia Rating scale (CDR) and GMS-AGECAT dementia. Divergent validity was examined with GMS-AGECAT depression.

RESULTS

The δ model demonstrated fit to the data, χ2(df) = 249.71(55), p < 0.001, CFI = 0.990, TLI = 0.997, RMSEA = 0.037. Latent variable δ was significantly associated with CDR and GMS-AGECAT dementia (range: β= 0.32 to 0.63), and was not associated with GMS-AGECAT depression. Compared to unadjusted models, δ model fit was poor when adjusted for age, gender, ethnicity, and education.

CONCLUSION

The study found some support for δ as a proxy for dementia in Singapore based on population data. Both convergent and divergent validity were established. In addition, the δ model structure appeared to be influenced by age, gender, ethnicity, and education covariates.

Welcome Back to Your Future: The Assessment of Dementia by the Latent Variable "δ"

The latent variable "δ" (for "dementia") has been proposed as a phenotype for all cause dementia. δ is extracted from cognitive batteries by a specific confirmatory factor analysis in a structural equation modeling framework. δ appears to be uniquely responsible for cognition's association with functional status. Because it is extracted from Spearman's general intelligence factor "g", this has broad implications for dementia's assessment and pathophysiology. This issue of the Journal of Alzheimer's Disease brings together several demonstrations of δ's psychometric properties by investigative groups from three continents. In their aggregate, they suggest that δ homologs may have far ranging applications in dementia's clinical assessment and biomarker selection.