Faculty Bibliography

OBJECTIVE:The impact of age of diabetes diagnosis on dementia risk across the life course is poorly characterized. We estimated the lifetime risk of dementia by age of diabetes diagnosis. RESEARCH DESIGN AND METHODS/METHODS:We included 13,087 participants from the Atherosclerosis Risk in Communities Study who were free from dementia at age 60 years. We categorized participants as having middle age-onset diabetes (diagnosis <60 years), older-onset diabetes (diagnosis 60-69 years), or no diabetes. Incident dementia was ascertained via adjudication and active surveillance. We used the cumulative incidence function estimator to characterize the lifetime risk of dementia by age of diabetes diagnosis while accounting for the competing risk of mortality. We used restricted mean survival time to calculate years lived without and with dementia. RESULTS:Among 13,087 participants, there were 2,982 individuals with dementia and 4,662 deaths without dementia during a median follow-up of 24.1 (percentile 25-percentile 75, 17.4-28.3) years. Individuals with middle age-onset diabetes had a significantly higher lifetime risk of dementia than those with older-onset diabetes (36.0% vs. 31.0%). Compared with those with no diabetes, participants with middle age-onset diabetes also had a higher cumulative incidence of dementia by age 80 years (16.1% vs. 9.4%) but a lower lifetime risk (36.0% vs. 45.6%) due to shorter survival. Individuals with middle age-onset diabetes developed dementia 4 and 1 years earlier than those without diabetes and those with older-onset diabetes, respectively. CONCLUSIONS:Preventing or delaying diabetes may be an important approach for reducing dementia risk throughout the life course.

OBJECTIVES/OBJECTIVE:To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. MATERIALS AND METHODS/METHODS:Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. RESULTS:ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. DISCUSSION/CONCLUSIONS:The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. CONCLUSION/CONCLUSIONS:These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.

OBJECTIVE:To estimate the prevalence of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) after infection with SARS-CoV-2 during pregnancy and to characterize associated risk factors. METHODS:In a multicenter cohort study (NIH RECOVER [Researching COVID to Enhance Recovery]-Pregnancy Cohort), individuals who were pregnant during their first SARS-CoV-2 infection were enrolled across the United States from December 2021 to September 2023, either within 30 days of their infection or at differential time points thereafter. The primary outcome was PASC , defined as score of 12 or higher based on symptoms and severity as previously published by the NIH RECOVER-Adult Cohort, at the first study visit at least 6 months after the participant's first SARS-CoV-2 infection. Risk factors for PASC were evaluated, including sociodemographic characteristics, clinical characteristics before SARS-CoV-2 infection (baseline comorbidities, trimester of infection, vaccination status), and acute infection severity (classified by need for oxygen therapy). Multivariable logistic regression models were fitted to estimate associations between these characteristics and presence of PASC. RESULTS:Of the 1,502 participants, 61.1% had their first SARS-CoV-2 infection on or after December 1, 2021 (ie, during Omicron variant dominance); 51.4% were fully vaccinated before infection; and 182 (12.1%) were enrolled within 30 days of their acute infection. The prevalence of PASC was 9.3% (95% CI, 7.9-10.9%) measured at a median of 10.3 months (interquartile range 6.1-21.5) after first infection. The most common symptoms among individuals with PASC were postexertional malaise (77.7%), fatigue (76.3%), and gastrointestinal symptoms (61.2%). In a multivariable model, the proportion PASC positive with vs without history of obesity (14.9% vs 7.5%, adjusted odds ratio [aOR] 1.65, 95% CI, 1.12-2.43), depression or anxiety disorder (14.4% vs 6.1%, aOR 2.64, 95% CI, 1.79-3.88) before first infection, economic hardship (self-reported difficulty covering expenses) (12.5% vs 6.9%, aOR 1.57, 95% CI, 1.05-2.34), and treatment with oxygen during acute SARS-CoV-2 infection (18.1% vs 8.7%, aOR 1.86, 95% CI, 1.00-3.44) were associated with increased prevalence of PASC. CONCLUSION/CONCLUSIONS:The prevalence of PASC at a median time of 10.3 months after SARS-CoV-2 infection during pregnancy was 9.3% in the NIH RECOVER-Pregnancy Cohort. The predominant symptoms were postexertional malaise, fatigue, and gastrointestinal symptoms. Several socioeconomic and clinical characteristics were associated with PASC after infection during pregnancy. CLINICAL TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov , NCT05172024.

PURPOSE OF REVIEW/OBJECTIVE:We summarize the latest evidence regarding the impact of plant-based diets on urological and planetary health to facilitate patient counseling and research regarding dietary intervention. RECENT FINDINGS/RESULTS:Studies have highlighted the association of plant-based diets with a lower risk of multiple urological conditions including prostate cancer, erectile dysfunction, benign prostatic hyperplasia, and nephrolithiasis, as well as benefits for planetary health. SUMMARY/CONCLUSIONS:Plant-based diets are associated with numerous benefits that co-promote urological and planetary health.

IMPORTANCE/UNASSIGNED:Half of all carriers of inherited cancer-predisposing variants in BRCA1 and BRCA2 are male, but the implications for their health are underrecognized compared to female individuals. Germline variants in BRCA1 and BRCA2 (also known as pathogenic or likely pathogenic variants, referred to here as BRCA1/2 PVs) are well known to significantly increase the risk of breast and ovarian cancers in female carriers, and knowledge of BRCA1/2 PVs informs established cancer screening and options for risk reduction. While risks to male carriers of BRCA1/2 PVs are less characterized, there is convincing evidence of increased risk for prostate cancer, pancreatic cancer, and breast cancer in males. There has also been a rapid expansion of US Food and Drug Administration-approved targeted cancer therapies, including poly ADP ribose polymerase (PARP) inhibitors, for breast, pancreatic, and prostate cancers associated with BRCA1/2 PVs. OBSERVATIONS/UNASSIGNED:This narrative review summarized the data that inform cancer risks, targeted cancer therapy options, and guidelines for early cancer detection. It also highlighted areas of emerging research and clinical trial opportunities for male BRCA1/2 PV carriers. These developments, along with the continued relevance to family cancer risk and reproductive options, have informed changes to guideline recommendations for genetic testing and strengthened the case for increased genetic testing for males. CONCLUSIONS AND RELEVANCE/UNASSIGNED:Despite increasing clinical actionability for male carriers of BRCA1/2 PVs, far fewer males than female individuals undergo cancer genetic testing. Oncologists, internists, and primary care clinicians should be vigilant about offering appropriate genetic testing to males. Identifying more male carriers of BRCA1/2 PVs will maximize opportunities for cancer early detection, targeted risk management, and cancer treatment for males, along with facilitating opportunities for risk reduction and prevention in their family members, thereby decreasing the burden of hereditary cancer.

OBJECTIVES/OBJECTIVE:To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (AI)-generated, patient-facing summaries of these findings. MATERIALS AND METHODS/METHODS:Radiology reports extracted from the electronic health record of a large academic medical center were manually reviewed to identify non-emergent, incidental findings with high likelihood of requiring follow-up, further sub-stratified as "definitely actionable" (DA) or "possibly actionable-clinical correlation" (PA-CC). Instruction prompts to GPT-4 were developed and iteratively optimized using a validation set of 50 reports. The optimized prompt was then applied to a test set of 430 unseen reports. GPT-4 performance was primarily graded on accuracy identifying either DA or PA-CC findings, then secondarily for DA findings alone. Outputs were reviewed for hallucinations. AI-generated patient-facing summaries were assessed for appropriateness via Likert scale. RESULTS:For the primary outcome (DA or PA-CC), GPT-4 achieved 99.3% recall, 73.6% precision, and 84.5% F-1. For the secondary outcome (DA only), GPT-4 demonstrated 95.2% recall, 77.3% precision, and 85.3% F-1. No findings were "hallucinated" outright. However, 2.8% of cases included generated text about recommendations that were inferred without specific reference. The majority of True Positive AI-generated summaries required no or minor revision. CONCLUSION/CONCLUSIONS:GPT-4 demonstrates proficiency in detecting actionable, incidental findings after refined instruction prompting. AI-generated patient instructions were most often appropriate, but rarely included inferred recommendations. While this technology shows promise to augment diagnostics, active clinician oversight via "human-in-the-loop" workflows remains critical for clinical implementation.

BACKGROUND:The spread of illicitly manufactured fentanyl is driving steep increases in US overdose deaths. Fentanyl seizures are correlated with state-level opioid-related mortality; however, more granular seizure surveillance information has the potential to better inform overdose prevention and harm reduction efforts. METHODS:Using data on fentanyl pill and powder seizures from High Intensity Drug Trafficking Areas (HIDTA), we tested associations between seizure prevalence and overdose mortality, from 2013 to 2020. The primary exposure-seizure burden-was constructed by identifying counties having high (above the median) prevalence of pill, powder, or combined pill/powder seizure burden per 100,000 population. Poisson models accounted for county demographic, law enforcement and time trends. RESULTS:During the timeframe, there were 13,842 fentanyl seizures in 606 US counties. In adjusted models, counties with a high burden of pill or powder fentanyl seizures, or both (combined pills/powder) exhibited higher total overdose mortality than non-high burden counties (pills adjusted prevalence ratio [aPR]: 1.10 [95 % confidence interval [CI]: 1.08, 1.12]; powder aPR 1.12 [CI: 1.11, 1.13]; combined pills/powder aPR: 1.27 [CI: 1.25, 1.29]). A similar pattern of associations with fentanyl seizure burden was noted for overdose deaths involving synthetic opioids (pills [aPR]: 0.99 [CI: 0.96, 1.02]; powder aPR 1.29 [CI: 1.27, 1.30]; combined pills/powder aPR 1.55 [CI: 1.52, 1.58]). CONCLUSIONS:Law enforcement data on fentanyl seizures predicts drug overdose mortality at the county-level. Integrating these data with more traditional epidemiologic surveillance approaches has the potential to inform community overdose response efforts.

Efforts to bolster the school readiness of Latinx children from low-income homes in the United States have focused on fostering parent engagement in children"™s education. Measurement of parent engagement in early childhood however, has been critiqued for having too narrow a focus on school-based involvement and missing other aspects of Latinx parent engagement. Using a recently developed culturally sensitive assessment of Latinx parent engagement, we test for associations between dimensions of Latinx parent engagement in learning and indicators of school readiness in a diverse sample of Latinx families (n = 114). We find significant associations between multiple dimensions of Latinx parent engagement and indicators of child school readiness. In addition to promoting parent-teacher connections, efforts to support Latinx school readiness equitably are encouraged to attend to various culturally relevant aspects of Latinx parent engagement in early childhood. In particular, investing in programing that supports parents"™ well-being and capacity for home-based engagement in learning may be a promising way to support the school readiness of Latinx children living in historically disinvested neighborhoods.

The wrong Supplementary file was originally published with this article; it has now been replaced with the correct file. The original article has been corrected.