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1. Predicting 5‐year dementia conversion in veterans with mild cognitive impairment

   https://doi.org/10.1002/dad2.12572  

   Irwin, Chase ; Tjandra, Donna ; Hu, Chengcheng ; Aggarwal, Vinod ; Lienau, Amanda ; Giordani, Bruno ; Wiens, Jenna ; Migrino, Raymond Q. ( March 2024 , Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring)

   Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all‐cause dementia (ACD) conversion at 5 years.

   Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held‐out data subset.

   Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors.

   EHR‐based prediction model had good performance in identifying 5‐year MCI to ACD conversion and has potential to assist triaging of at‐risk patients.

   Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all‐cause dementia within 5 years.

   Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18).

   Age and vascular‐related morbidities were predictors of dementia conversion.

   Synthetic data was comparable to real data in modeling MCI to dementia conversion.

   An electronic health record–based model using demographic and co‐morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all‐cause dementia (ACD) within 5 years.

   Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5‐year conversion from MCI to ACD.

   High body mass index, alcohol abuse, and sleep apnea were protective factors for 5‐year conversion from MCI to ACD.

   Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health‐care data with minimized patient privacy concern that could accelerate scientific discoveries.

    

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2. A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring

   https://doi.org/10.1038/s41598-024-52461-1  

   Barua, Souptik ; Glantz, Namino ; Larez, Arianna ; Bevier, Wendy ; Sabharwal, Ashutosh ; Kerr, David ( February 2024 , Scientific Reports)

   In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA_1clevels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37–63%)] compared to pre-T2D [36% (95% CI 31–48%), p = 0.01] and at-risk participants [34% (95% CI 27–39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA_1csub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.

    

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3. Shared exposure liability of type 2 diabetes and other chronic conditions in the UK Biobank

   https://doi.org/10.1007/s00592-022-01864-5  

   He, Yixuan ; Patel, Chirag J. ( March 2022 , Acta Diabetologica)

   To investigate whether the cumulative exposure risks of incident T2D are shared with other common chronic diseases.

   We first establish and report the cross-sectional prevalence, cross-sectional co-prevalence, and incidence of seven T2D-associated chronic diseases [hypertension, atrial fibrillation, coronary artery disease, obesity, chronic obstructive pulmonary disease (COPD), and chronic kidney and liver diseases] in the UK Biobank. We use published weights of genetic variants and exposure variables to derive the T2D polygenic (PGS) and polyexposure (PXS) risk scores and test their associations to incident diseases.

   PXS was associated with higher levels of clinical risk factors including BMI, systolic blood pressure, blood glucose, triglycerides, and HbA1c in individuals without overt or diagnosed T2D. In addition to predicting incident T2D, PXS and PGS were significantly and positively associated with the incidence of all 7 other chronic diseases. There were 4% and 8% of individuals in the bottom deciles of PXS and PGS, respectively, who were prediabetic at baseline but had low risks of T2D and other chronic diseases. Compared to the remaining population, individuals in the top deciles of PGS and PXS had particularly high risks of developing chronic diseases. For instance, the hazard ratio of COPD and obesity for individuals in the top T2D PXS deciles was 2.82 (95% CI 2.39–3.35,P = 4.00 × 10⁻³³) and 2.54 (95% CI 2.24–2.87,P = 9.86 × 10⁻⁵⁰), respectively, compared to the remaining population. We also found that PXS and PGS were both significantly (P < 0.0001) and positively associated with the total number of incident diseases.

   T2D shares polyexposure risks with other common chronic diseases. Individuals with an elevated genetic and non-genetic risk of T2D also have high risks of cardiovascular, liver, lung, and kidney diseases.

    

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4. Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers

   https://doi.org/10.1002/alz.12676  

   Tjandra, Donna ; Migrino, Raymond Q. ; Giordani, Bruno ; Wiens, Jenna ( April 2022 , Alzheimer's & Dementia)

   Studies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer's disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations.

   In a retrospective cohort study, EHR data from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM).

   The VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM).

   Approaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.

    

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5. Association of type 2 diabetes mellitus with dementia‐related and non–dementia‐related mortality among postmenopausal women: A secondary competing risks analysis of the women's health initiative

   https://doi.org/10.1002/alz.13416  

   Titcomb, Tyler J. ; Richey, Phyllis ; Casanova, Ramon ; Phillips, Lawrence S. ; Liu, Simin ; Karanth, Shama D. ; Saquib, Nazmus ; Nuño, Tomas ; Manson, JoAnn E. ; Shadyab, Aladdin H. ; et al ( August 2023 , Alzheimer's & Dementia)

   Alzheimer's disease (AD) and AD‐related dementias (ADRD) are leading causes of death among older adults in the United States. Efforts to understand risk factors for prevention are needed.

   Participants (n = 146,166) enrolled in the Women's Health Initiative without AD at baseline were included. Diabetes status was ascertained from self‐reported questionnaires and deaths attributed to AD/ADRD from hospital, autopsy, and death records. Competing risk regression models were used to estimate the cause‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the prospective association of type 2 diabetes mellitus (T2DM) with AD/ADRD and non‐AD/ADRD mortality.

   There were 29,393 treated T2DM cases and 8628 AD/ADRD deaths during 21.6 (14.0–23.5) median (IQR) years of follow‐up. Fully adjusted HRs (95% CIs) of the association with T2DM were 2.94 (2.76–3.12) for AD/ADRD and 2.65 (2.60–2.71) for the competing risk of non‐AD/ADRD mortality.

   T2DM is associated with AD/ADRD and non‐AD/ADRD mortality.

   Type 2 diabetes mellitus is more strongly associated with Alzheimer's disease (AD)/AD and related dementias (ADRD) mortality compared to the competing risk of non‐AD/ADRD mortality among postmenopausal women.

   This relationship was consistent for AD and ADRD, respectively.

   This association is strongest among participants without obesity or hypertension and with younger age at baseline, higher diet quality, higher physical activity, higher alcohol consumption, and older age at the time of diagnosis of type 2 diabetes mellitus.

    

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