Publications by Year: 2025

BACKGROUND AND OBJECTIVES: Genetic testing in epilepsy has become increasingly available, and recommendations for its use have been set forth by professional society guidelines. The development of a user-friendly risk prediction model may aid providers in selecting adult patients with a high likelihood of receiving a positive genetic test result.

METHODS: Adults who underwent multigene panel testing for epilepsy from March 2016 to June 2024 were divided into a training (n = 1449) and a testing set (n = 1450). We developed prediction models based on clinical characteristics using logistic regression and FasterRisk scores for positive genetic tests and evaluated their performance.

RESULTS: The prediction models had poor discriminative power and failed to predict positive results, suggesting that conventional clinical characteristics (sex, intellectual disability, developmental delay, autism, medically refractory epilepsy, family history of epilepsy, and age at seizure onset) are insufficient for selecting patients for genetic testing.

DISCUSSION: Our findings suggest that routine genetic testing may be broadly warranted for adults with unexplained epilepsy, as clinical characteristics alone appear unable to reliably identify which patients are likely to have positive results on multigene panels. Future models may benefit from incorporating physical exam findings, neuroimaging, and electroencephalogram data, as well as larger training sets.

Metabolic dysfunction-associated steatohepatitis (MASH) and type 2 diabetes mellitus (T2DM) are closely linked conditions that share common disturbances in metabolism, inflammation, and fibrotic processes. MASH is characterized by fat accumulation in the liver, hepatocyte damage, and progressive fibrosis, whereas T2DM involves insulin resistance and impaired beta-cell function. The coexistence of these disorders creates a liver and pancreas feedback loop, in which impaired hepatic insulin signaling worsens blood glucose control and high glucose levels further damage the liver. Key cellular contributors include hepatocytes, Kupffer cells, hepatic stellate cells, and pancreatic β-cells, while non-coding RNAs influence lipid metabolism and inflammation. Emerging therapies, including GLP1 receptor agonists, dual incretin agents, PPAR modulators, thyroid hormone receptor beta modulators, FXR agonists, and FGF analogues, along with lifestyle interventions, show promise in improving both liver and metabolic outcomes. Precision medicine approaches may further refine individualized treatment strategies.

IMPORTANCE: The US Preventive Services Task Force (USPSTF) recommends lung cancer screening for individuals aged 50 to 80 years with a smoking history of at least 20 pack-years and 15 quit-years or less. This recommendation implies that former heavy smokers with at least 20 pack-years would be offered screening from 50 years of age onward or not at all, depending on a dichotomous classification by time since smoking cessation.

OBJECTIVE: To develop a more personalized lung cancer screening strategy that reflects individual risks by defining risk-adapted starting ages for screening according to time since cessation.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used longitudinal data from the population-based UK Biobank, which recruited participants in England, Wales, and Scotland between 2006 and 2010. Participants were 50 years or older, had no prior cancer diagnosis, and had at least 20 pack-years of smoking history. The following censoring dates were applied: December 31, 2020, for England; November 30, 2021, for Scotland; and December 31, 2016, for Wales. The present analysis was performed in April 2025.

EXPOSURE: Duration of smoking cessation (categorized into ≤5, 6-10, 11-15, and >15 quit-years).

MAIN OUTCOMES AND MEASURES: The association between smoking cessation time, pack-years, age, and 10-year lung cancer incidence was assessed using multivariable extended Cox proportional hazards models. Risk postponement periods (RPPs) were estimated from the regression coefficients for time since cessation and age. These RPP estimates were used to derive risk-adapted starting ages for lung cancer screening among former heavy smokers, using the USPSTF-proposed starting age of 50 years as a reference for the risk level of continuing heavy smokers.

RESULTS: A total of 86 035 former or current heavy smokers (mean [SD] age, 60.8 [5.3] years; range, 50-72 years), of whom 2109 (2.5%) were diagnosed with lung cancer, were included in the analysis. The RPPs for smoking cessation were 2.7 (95% CI, 0.0-5.3), 6.2 (95% CI, 4.4-7.9), 10.4 (95% CI, 8.5-12.4), and 17.1 (95% CI, 15.0-19.2) years for former heavy smokers who quit at least 5, 6 to 10, 11 to 15, and more than 15 years ago, respectively. This could translate to risk-adapted screening starting ages ranging from 53 (95% CI, 50-55) years to 67 (95% CI, 65-69) years, if current smokers begin screening at age 50.

CONCLUSIONS AND RELEVANCE: This cohort study of the UK Biobank provides an empirical basis for risk-adapted starting ages for lung cancer screening among former heavy smokers.

The current approach for investigating patients with suspected inborn errors of metabolism (IEMs) involves traditional targeted biochemical assays such as amino/organic acid analyses. Although highly effective for confirmatory testing, they are less effective in identifying disorders not included in newborn screening (NBS) panels, and for patients with non-classical clinical presentations. Targeted assays analyze a narrow range of metabolites and are conducted across different analytical platforms, often requiring more than one specimen type. In contrast, comprehensive metabolic profiling using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) provides significantly more information from a single specimen, eliminating the need for multiple and time-consuming analyses across different platforms. We describe the use of LC-HRMS metabolic profiling in two patients with decompensated maple syrup urine disease (MSUD). In the first patient, a previously healthy 3-month-old infant presenting with altered mental status, apnea, and seizures, LC-HRMS analysis of plasma before treatment showed increased levels of branched-chain amino acids and their related 2-keto and hydroxy acids. The diagnosis of MSUD was confirmed by targeted amino acid analysis. Additionally, the treatment course, which included dialysis and nutritional management, was monitored using LC-HRMS. This approach was successfully applied to a second patient, a 1-week-old infant with classical MSUD identified through NBS. In conclusion, comprehensive metabolic profiling by LC-HRMS is a valuable investigative tool for patients with both classic and non-specific neurometabolic clinical phenotypes, providing additional insights into metabolite perturbations during acute management.

Abortion doulas support patients who are receiving abortion care. Understanding the abortion doula landscape is critical for patients, clinicians, doulas, and those interested in expanding programs for doula support. We aimed to describe the state of US-based abortion doula programs through a mapping review of the grey literature, documenting programs' characteristics from their online presence (websites, Facebook, Instagram, X). We identified 17 active abortion doula programs. In addition, we identified 30 programs which appeared either to be inactive or to not provide abortion support, primarily in states with post-Dobbs abortion bans. Most active abortion doula programs are community-based, volunteer-run, and free for patients. Three doula programs have partnerships with local clinics; three clinics have in-house doula programs. Most train their own doulas. Most US-based abortion doula programs independently provide community-based, no-cost, volunteer doula services.

IMPORTANCE: Unruptured intracranial aneurysms (UIAs) affect 3.2% of the general population, and approximately 85% of subarachnoid hemorrhages result from their rupture. Despite their classification as low risk by prediction tools such as PHASES (population, hypertension, age, size of aneurysm, earlier subarachnoid hemorrhage from another aneurysm, and site of aneurysm) and the Unruptured Cerebral Aneurysm Study (UCAS), UIAs less than 10 mm are susceptible to rupture.

OBJECTIVE: To develop and externally validate a machine-learning model (MLM) predicting rupture risk of UIAs.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective multicenter prognostic study analyzed UIAs from 4 institutions across 3 continents from January 2003 to November 2022. Each UIA was characterized by 29 clinical and 18 morphological variables. For model development, patients with UIAs were drawn from a large institutional cohort. Statistical analysis was performed from April 2024 to March 2025.

EXPOSURE: An MLM based on the Light Gradient Boosting Machine algorithm was trained, and performance was assessed for validation externally.

MAIN OUTCOMES AND MEASURES: The primary outcome was aneurysm rupture within 2 years after risk evaluation. Model performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and the area under the receiver operating characteristic curve (AUROC) with 95% CIs.

RESULTS: Drawing from 11 579 UIAs from multiple institutions of 8846 patients, there were 2750 patients with 3312 UIAs in the development cohort (median [IQR] age, 65.3 [54.9-73.6] years; 1856 females [67.5%]) and 1153 patients with 1501 UIAs in the external cohort (median [IQR] age, 63.6 [53.9-70.9] years; 828 females [71.8%]), for whom the MLM demonstrated a robust performance in risk estimation. In the development cohort, 71 UIAs (2.1%) ruptured during 8.5 years' median follow-up (IQR, 5.1-11.6 years), and in the external cohort, 48 UIAs (3.2%) ruptured during a median (IQR) follow-up of 5.4 (3.7-8.7) years. With a threshold of 0.52 for the MLM, performance in the development cohort for sensitivity was 0.78 (95% CI, 0.67-0.86); specificity, 0.82 (95% CI, 0.80-0.83); PPV, 0.09 (95% CI, 0.07-0.11); NPV, 0.99 (95% CI, 0.99-1.00); PLR, 4.23 (95% CI, 3.66-4.89); NLR, 0.28 (95% CI, 0.18-0.42); and AUROC, 0.88 (95% CI, 0.85-0.92) and in the external cohort, sensitivity was 0.90 (95% CI, 0.78-0.95); specificity, 0.70 (95% CI, 0.67-0.72); PPV, 0.09 (95% CI, 0.07-0.12); NPV, 1.00 (95% CI, 0.99-1.00); PLR, 2.94 (95% CI, 2.60-3.33); NLR, 0.15 (95% CI, 0.07-0.34); and AUROC, 0.90 (95% CI, 0.85-0.94). The MLM performed consistently in UIAs less than 10 mm (AUROC, 0.88 [95% CI, 0.82-0.94]), suggesting potential complementary value to PHASES and the UCAS.

CONCLUSIONS AND RELEVANCE: In this prognostic study, the MLM consistently identified features of UIAs as significantly associated with rupture across different cohorts. These results support the MLM's potential to assist physicians and patients in UIA treatment decisions.

IMPORTANCE: In 2017, British Columbia guidelines changed the preferred first-line treatment for opioid use disorder from methadone to buprenorphine/naloxone. Systematic reviews have consistently shown better treatment retention associated with methadone, while studies have reported a reduced risk of mortality associated with buprenorphine/naloxone, causing confusion in interpretation for clinical guidelines.

OBJECTIVE: To estimate the population-level health benefits and harms associated with buprenorphine/naloxone vs methadone for treatment of opioid use disorder, accounting for differences in treatment retention and risk of mortality while receiving treatment.

DESIGN, SETTING, AND PARTICIPANTS: This decision analytic model analysis was conducted from January 1, 2010, to March 17, 2020, using a semi-Markov cohort model and linked population-level health administrative data on all individuals presenting for opioid agonist treatment in British Columbia, Canada. Hazard ratios were estimated for the association of buprenorphine/naloxone vs methadone with treatment discontinuation (breaks in dispensations lasting ≥5 days for methadone and ≥6 days for buprenorphine/naloxone) and mortality while receiving treatment (per protocol). Treatment episodes in primary analysis were based on the medication initiated (initiator analysis). Probabilistic (mean estimates and 95% credible intervals from 10 000 simulations) and deterministic sensitivity analyses were conducted. Data were analyzed between August 2023 and October 2024.

EXPOSURE: Alternative treatment policies in which buprenorphine/naloxone or methadone were exclusively available to individuals presenting for opioid agonist treatment.

MAIN OUTCOME AND MEASURES: Incremental life-years, fatal overdoses, and all-cause deaths.

RESULTS: The study population included 40 461 individuals with 109 126 cumulative person-years of follow-up (median [IQR] age, 33 [23-43] years; 66.0% [range, 56.0%-76.0%] male). A policy of exclusively buprenorphine/naloxone was estimated to have -1602 incremental life-years (95% credible interval, -3249 to -549 life-years) compared with methadone, with an additional 221 fatal overdoses (95% credible interval, 119 to 376 overdoses) and 303 all-cause deaths (95% credible interval, 120 to 589 deaths) over a 10-year period.

CONCLUSIONS AND RELEVANCE: Results from this study suggest that any advantages from a reduced risk of mortality with treatment with buprenorphine/naloxone were outweighed by deficits in treatment retention. Evaluated in a jurisdiction where both medications were available in office-based settings, these findings do not support recommendations of buprenorphine/naloxone as first-line treatment over methadone.

IMPORTANCE: Prior studies report negative associations between prenatal exposure to fine particulate matter (ie, aerodynamic diameter <2.5 µg; PM2.5) and birth weight, but have typically averaged exposure across pregnancy, which may not reveal windows of susceptibility.

OBJECTIVE: To identify windows of prenatal susceptibility to PM2.5.

DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective analysis of a prospectively enrolled cohort study. Participants were enrolled at 1 of 50 sites participating in the US Environmental Influences on Child Health Outcomes Cohort. The study included full-term, singleton births occurring between September 2003 and December 2021. Statistical analyses were conducted from March 2024 to February 2025.

EXPOSURES: Daily residential PM2.5 exposure was estimated using a machine-learning model covering the contiguous US and mean exposure estimates were calculated for each week of pregnancy.

MAIN OUTCOMES AND MEASURES: Bayesian distributed lag interaction models were used to examine cumulative and week-specific associations between PM2.5 exposure and birth weight for gestational age (BWGA) z scores. Interactions with sex, race and ethnicity, and region were also examined.

RESULTS: The sample of 16 868 mother-newborn pairs (maternal mean [SD] age, 30.4 [5.5] years; 605 [3.6%] Asian, 2197 [13.0%] Black or Black-Hispanic, 3407 [20.2%] Hispanic, 9251 [54.8%] non-Hispanic White, and 1408 [8.4%] other) included 15 806 unique mothers and 1062 mothers with 2 or more children in the study. Mean (SD) weekly PM2.5 exposure during pregnancy was relatively low, at 8.03 (2.3) µg/m3, and overall mean (SD) birth weight was 3410.7 (464.5) g. In the sample overall, there was a negative association between PM2.5 exposure and BWGA z score (β = -0.06; 95% credible interval [CrI], -0.10 to -0.03), with a critical window in early gestation (weeks 1-5) that persisted only among males (β = -0.06; 95% CrI, -0.10 to -0.02). When examining differences by region, there were negative associations in the Northeast (β = -0.09; 95% CrI, -0.15 to -0.03), Midwest (β = -0.11; 95% CrI, -0.17 to -0.05; critical window, 12-18 weeks), and South (β = -0.18; 95% CrI, -0.17 to -0.05; critical window, 3-9 weeks).

CONCLUSIONS AND RELEVANCE: In this cohort study, higher PM2.5 exposure was associated with lower BWGA z score, with critical windows identified during early pregnancy to midpregnancy; however, findings varied by sex and region. Understanding windows of susceptibility to environmental exposures can help guide research on underlying biological processes and can inform strategies for limiting exposure during certain periods of pregnancy.