Utility of the NIH Toolbox Cognition Battery in middle to older aged adults with longstanding type 1 diabetes: The DCCT/EDIC study
Bibliographic record
Abstract
Objective: Adults with type 1 diabetes (T1D) face an increased risk for cognitive decline and dementia. Diabetes-related and vascular risk factors have been linked to cognitive decline using detailed neuropsychological testing; however, it is unclear if cognitive screening batteries can detect cognitive changes associated with aging in T1D. Method: 1,049 participants with T1D (median age 59 years; range 43–74) from the Diabetes Control and Complications Trial (DCCT), and the follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study, completed the NIH Toolbox Cognition Battery (NIHTB-C) and Montreal Cognitive Assessment (MoCA). Neuropsychological assessments, depression, glycated hemoglobin levels (HbA1c), severe hypoglycemia, T1D complications, and vascular risk factors were assessed repeatedly over 32 years to determine associations with current NIHTB-C performance. Available cognitive data was clinically adjudicated to determine cognitive impairment status. Results: NIHTB-C scores had moderate associations (r = 0.36–0.53) with concurrently administered neuropsychological tests. In multivariate models, prior severe hypoglycemic episodes, depression symptoms, nephropathy, lower BMI, and higher HbA1c and LDL cholesterol were associated with poorer NIHTB-C Fluid Cognition Composite scores. The NIHTB-C adequately detected adjudicated cognitive impairment (Area Under the Curve = 0.86; optimal cut score ≤90). The MoCA performed similarly (Area Under the Curve = 0.83; optimal cut score ≤25). Conclusions: The NIHTB-C is sensitive to the cognitive effects of diabetes-related and vascular risk factors, correlated with neuropsychological testing, and accurately detects adjudicated cognitive impairment. These data support its use as a screening test in middle to older aged adults with T1D to determine if referral for detailed neuropsychological assessment is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".