Corneal nerve loss predicts dementia in patients with mild cognitive impairment.

OBJECTIVES: This study compared the utility of corneal nerve measures with brain volumetry for predicting progression to dementia in individuals with mild cognitive impairment (MCI). METHODS: Participants with no cognitive impairment (NCI) and MCI underwent assessment of cognitive function, brain volumetry of thirteen brain structures, including the hippocampus and corneal confocal microscopy (CCM). Participants with MCI were followed up in the clinic to identify progression to dementia. RESULTS: Of 107 participants with MCI aged 68.4 ± 7.7 years, 33 (30.8%) progressed to dementia over 2.6-years of follow-up. Compared to participants with NCI (n = 12), participants who remained with MCI (n = 74) or progressed to dementia had lower corneal nerve measures (p < 0.0001). Progressors had lower corneal nerve measures, hippocampal, and whole brain volume (all p < 0.0001). However, CCM had a higher prognostic accuracy (72%-75% vs 68%-69%) for identifying individuals who progressed to dementia compared to hippocampus and whole brain volume. The adjusted odds ratio for progression to dementia was 6.1 (95% CI: 1.6-23.8) and 4.1 (95% CI: 1.2-14.2) higher with abnormal CCM measures, but was not significant for abnormal brain volume. INTERPRETATION: Abnormal CCM measures have a higher prognostic accuracy than brain volumetry for predicting progression from MCI to dementia. Further work is required to validate the predictive ability of CCM compared to other established biomarkers of dementia.

Loss of corneal nerves and brain volume in mild cognitive impairment and dementia.

Introduction: This study compared the capability of corneal confocal microscopy (CCM) with magnetic resonance imaging (MRI) brain volumetry for the diagnosis of mild cognitive impairment (MCI) and dementia. Methods: In this cross-sectional study, participants with no cognitive impairment (NCI), MCI, and dementia underwent assessment of Montreal Cognitive Assessment (MoCA), MRI brain volumetry, and CCM. Results: Two hundred eight participants with NCI (n = 42), MCI (n = 98), and dementia (n = 68) of comparable age and gender were studied. For MCI, the area under the curve (AUC) of CCM (76% to 81%), was higher than brain volumetry (52% to 70%). For dementia, the AUC of CCM (77% to 85%), was comparable to brain volumetry (69% to 93%). Corneal nerve fiber density, length, branch density, whole brain, hippocampus, cortical gray matter, thalamus, amygdala, and ventricle volumes were associated with cognitive impairment after adjustment for confounders (All P's < .01). Discussion: The diagnostic capability of CCM compared to brain volumetry is higher for identifying MCI and comparable for dementia, and abnormalities in both modalities are associated with cognitive impairment.

The Linkage Between Bone Densitometry and Cardiovascular Disease.

Dual-energy X-ray absorptiometry (DXA) has been traditionally used to assess body composition covering bone, fat and muscle content. Cardiovascular disease (CVD) has deleterious effects on bone health and fat composition. Therefore, early detection of bone health, fat and muscle composition would help to anticipate a proper diagnosis and treatment plan for CVD patients. In this study, we leveraged machine learning (ML)-based models to predict CVD using DXA, demonstrating that it can be considered an innovative approach for early detection of CVD. We leveraged state-of-the-art ML models to classify the CVD group from non-CVD group. The proposed logistic regression-based model achieved nearly 80% accuracy. Overall, the bone mineral density, fat content, muscle mass and bone surface area measurements were elevated in the CVD group compared to non-CVD group. Ablation study revealed a more successful discriminatory power of fat content and bone mineral density than muscle mass and bone areas. To the best of our knowledge, this work is the first ML model to reveal the association between DXA measurements and CVD in the Qatari population. We believe this study will open new avenues of introducing DXA in creating the diagnosis and treatment plan of cardiovascular diseases.