GABA, Aging and Exercise: Functional and Intervention Considerations.

The global growth of an aging population is expected to coincide with an increase in aging-related pathologies, including those related to brain health. Thus, the potential for accelerated cognitive health declines due to adverse aging is expected to have profound social and economic implications. However, the progression to pathological conditions is not an inevitable part of aging. In fact, engaging in activities that improve cardiovascular fitness appears to be a means that offers the benefits of maintaining and/or improving cognitive health in older age. However, to date, the underlying mechanisms responsible for improved central nervous system health and function with exercise are not yet fully elucidated. Consequently, there is considerable interest in studies aimed at understanding the neurophysiological benefits of exercise on aging. One such area of study suggests that the improvements in brain health via exercise are, in part, driven by the recovery of inhibitory processes related to the neurotransmitter gamma-aminobutyric acid (GABA). In the present review, we highlight the opposing effects of aging and exercise on cortical inhibition and the GABAergic system's functional integrity. We highlight these changes in GABA function by reviewing work with in vivo measurements: transcranial magnetic stimulation (TMS) and magnetic resonance spectroscopy (MRS). We also highlight recent and significant technological and methodological advances in assessing the GABAergic system's integrity with TMS and MRS. We then discuss potential future research directions to inform mechanistic GABA study targeted to improve health and function in aging. We conclude by highlighting the significance of understanding the effects of exercise and aging, its influence on GABA levels, and why a better understanding is crucial to allow for more targeted and effective interventions aimed to ultimately improve age-related decline in aging.

Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping.

Importance: Developmental language disorder (DLD) is a common (with up to 7% prevalence) yet underdiagnosed childhood disorder whose underlying biological profile and comorbidities are not fully understood, especially at the population level. Objective: To identify clinically relevant conditions that co-occur with DLD at the population level. Design, Setting, and Participants: This case-control study used an electronic health record (EHR)-based population-level approach to compare the prevalence of comorbid health phenotypes between DLD cases and matched controls. These cases were identified using the Automated Phenotyping Tool for Identifying Developmental Language Disorder algorithm of the Vanderbilt University Medical Center EHR, and a phenome enrichment analysis was used to identify comorbidities. An independent sample was selected from the Geisinger Health System EHR to test the replication of the phenome enrichment using the same phenotyping and analysis pipeline. Data from the Vanderbilt EHR were accessed between March 2019 and October 2020, while data from the Geisinger EHR were accessed between January and March 2022. Main Outcomes and Measures: Common and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis. Results: Comorbidity analysis was conducted for 5273 DLD cases (mean [SD] age, 16.8 [7.2] years; 3748 males [71.1%]) and 26 353 matched controls (mean [SD] age, 14.6 [5.5] years; 18 729 males [71.1%]). Relevant phenotypes associated with DLD were found, including learning disorder, delayed milestones, disorders of the acoustic nerve, conduct disorders, attention-deficit/hyperactivity disorder, lack of coordination, and other motor deficits. Several other health phenotypes not previously associated with DLD were identified, such as dermatitis, conjunctivitis, and weight and nutrition, representing a new window into the clinical complexity of DLD. Conclusions and Relevance: This study found both rare and common comorbidities of DLD. Comorbidity profiles may be leveraged to identify risk of additional health challenges, beyond language impairment, among children with DLD.

Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.

Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material https://doi.org/10.23641/asha.12753578.