# Analyzing Shared Cognitive Risk Factors in Comorbidity

> **NIH NIH P50** · UNIVERSITY OF COLORADO · 2020 · $198,589

## Abstract

PROJECT SUMMARY/ABSTRACT
Comorbidity is pervasive among the learning disabilities (LDs) (comorbidity rates of 25-50%) and is a key
predictor of academic and functional outcomes, yet little is known about the cognitive mechanisms that
increase a child’s risk for multiple disorders. This project proposes a multiple deficit model of LDs where
shared cognitive risk factors contribute to comorbidity of reading and math (basic and higher-order skills) and
ADHD. The overall goal of this project is to use converging methods, including latent modeling, experimental
methods, and behavioral genetics, to identify the role of shared cognitive risk factors in the comorbidity of LDs
and ADHD. Potential shared cognitive deficits between learning disabilities and ADHD include processing
speed (PS), executive functions (EFs), and specific domains of implicit learning. Although these factors have
each been examined in LDs individually, this is the first study to assess the impact of these related constructs
on comorbidity across LDs. One challenge with the PS construct is the “task impurity” of the measures. To
address this concern, we will systematically examine 4 theoretical models (some non-exclusive) about the role
of PS in LDs and ADHD: (1) PS is reducible to cognitive g, (2) PS is reducible to EF, (3) PS is a domain-
general factor, (4) PS has content-specific components that are associated with specific academic skills. Aim 1
will examine these 4 models by introducing new experimental PS tasks and leveraging existing data with
advanced latent models of the shared and unique variance among the constructs. Another potential shared
cognitive risk factor among learning disabilities are deficits in implicit learning, specifically the subdomains of
procedural learning and statistical learning, which we have not yet studied in this sample. Aim 1 will include
new measures of procedural (motor-based) and statistical (language-based) learning. This will be the first
study to examine the association of these constructs with multiple LDs and ADHD. Because this LD center
employs a genetically-sensitive twin design, we will be able to draw genetic inferences from the best-fitting
cognitive models. In Aim 2, we will determine whether shared cognitive risk factors also share genetic
relationships with the comorbid LDs. The population of bilingual youth is growing exponentially in the US, yet
we still know very little about academic development in this population. In Aim 3, we plan to test the best-fitting
multiple deficit model from Aim 1 in a sample of bilingual, Hispanic youth. This analysis will be an important
test of the universality of the multiple deficit model where points of divergence will have important clinical
implications. The focus of this project on identifying and dissecting shared cognitive risk factors for LDs and
ADHD has clinical relevance for assessment of comorbidity risk and for novel treatment targets that may
impact generalized cognitive risk mecha...

## Key facts

- **NIH application ID:** 10011587
- **Project number:** 5P50HD027802-29
- **Recipient organization:** UNIVERSITY OF COLORADO
- **Principal Investigator:** BRUCE F PENNINGTON
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $198,589
- **Award type:** 5
- **Project period:** 1996-12-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10011587

## Citation

> US National Institutes of Health, RePORTER application 10011587, Analyzing Shared Cognitive Risk Factors in Comorbidity (5P50HD027802-29). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10011587. Licensed CC0.

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