Publications

2024

Bhatia, Roma, Mark A Hernandez, Jonathan Platt, Anne B Newman, David S Siscovick, Kenneth J Mukamal, and Gina S Lovasi. (2024) 2024. “Associations of Neighbourhood Food Retail With Disability and Death in Older Adults: Cardiovascular Health Study.”. BMJ Nutrition, Prevention & Health 7 (2): e000646. https://doi.org/10.1136/bmjnph-2023-000646.

BACKGROUND: A healthier diet is associated with lower chronic disease burden, but the impact of neighbourhood food environments on disability and death in older adults is not known.

METHODS: In the Cardiovascular Health Study, a cohort study of adults aged 65+, we calculated study years until death (years of life (YOL)), study years without activities of daily living (ADL) difficulty (years of able life; YoAL) and percent of study years without ADL difficulty (compression of disability). Linear regression quantified associations of food establishments within 5 km of baseline home address (as a z-score) with each outcome, adjusted for sociodemographic characteristics. Sensitivity analyses considered adjustment for risk factors and comorbidities, multiple imputation, alternate neighbourhood definitions (1-km radial buffer, census tract) and restriction on residential stability.

RESULTS: We included 4298 participants followed for up to 26 years. All food retail establishments were associated with 6 months higher YoAL per SD in the main model (beta, 0.50 years; 95% CI 0.01, 0.98; p=0.046), with similar findings across sensitivity analyses except when restricting on residential stability. Supermarkets and produce markets were associated with compression of disability (beta, 2.31; 95% CI, 0.04, 4.57) and when using 1-km buffers with YOL (beta, 0.23 years; 95% CI 0.03, 0.43) and YoAL (beta, 0.21 years; 95% CI 0.01, 0.41). Non-supermarket food stores were associated with YoAL (beta, 0.67 years; 95% CI, 0.07, 1.27) and compression of disability (beta, 3.03; 95% CI 0.44, 5.62), but significance was not consistent across sensitivity analyses. Fast-food restaurants did not reach statistical significance in any model.

CONCLUSION: All food retail was associated with YOL without impairment. Neighbourhood food retail access and type may both have roles in extending YOL and years of able life among older adults, but the findings were sensitive to decisions made during measurement and modelling.

Rainer, Katharine W, William Earle, Erin D Michos, Edgar R Miller, Amal A Wanigatunga, Heather Rebuck, Robert Christensen, et al. (2024) 2024. “Effects of Vitamin D Supplementation on Cardiac Biomarkers: Results from the STURDY Trial.”. American Journal of Preventive Cardiology 20: 100871. https://doi.org/10.1016/j.ajpc.2024.100871.

OBJECTIVES: In observational studies, older adults with low serum vitamin D levels are at higher risk of cardiovascular disease (CVD), but randomized trials have failed to demonstrate reduction in CVD risk from vitamin D supplementation, possibly because the doses of vitamin D supplements tested were too low. Our objective was to determine if higher doses of vitamin D supplementation reduce high-sensitivity cardiac troponin (hs-cTnI) and N-terminal pro-b-type natriuretic peptide (NT-proBNP), markers of subclinical CVD.

METHODS: The Study to Understand Fall Reduction and Vitamin D in You (STURDY) was a double-blind, randomized, response-adaptive trial that tested the effects of 4 doses of vitamin D3 supplementation (200, 1000, 2000, 4000 IU/day) on fall risk among older adults with low serum 25-hydroxyvitamin D concentrations (10-29 ng/mL). Hs-cTnI and NT-proBNP levels were measured at baseline, 3-, 12-, and 24-month follow-up visits. For this ancillary study, we used data from the original trial and compared participants by treatment group: low-dose (200 IU/day) or high-dose (1000+ IU/day). The effects of vitamin D dose on biomarkers were assessed via mixed effects tobit models.

RESULTS: Among 688 participants (mean age of 76.5) hs-cTnI increased in both the low- and high-dose groups by 5.2 % and 7.0 %, respectively; likewise, NT-proBNP increased by 11.3 % and 9.3 %, respectively. Compared to the low-dose, high-dose vitamin D supplementation did not affect hs-cTnI (1.6 %-difference; 95 % CI: -5.3, 8.9) or NT-proBNP (-1.8 %-difference; 95 % CI: -9.3, 6.3).

CONCLUSIONS: Compared to low-dose vitamin D supplementation, doses ≥1,000 IU/ day did not affect markers of subclinical CVD in older adults with low serum vitamin D levels.

Li, Zeyu, Mingyu Zhang, Xiumei Hong, Guoying Wang, Giehae Choi, Kari C Nadeau, Jessie P Buckley, and Xiaobin Wang. (2024) 2024. “Cord Plasma Metabolomic Signatures of Prenatal Per- and Polyfluoroalkyl Substance (PFAS) Exposures in the Boston Birth Cohort.”. Environment International 194: 109144. https://doi.org/10.1016/j.envint.2024.109144.

BACKGROUND: Prenatal per- and polyfluoroalkyl substance (PFAS) exposures are associated with adverse offspring health outcomes, yet the underlying pathological mechanisms are unclear. Cord blood metabolomics can identify potentially important pathways associated with prenatal PFAS exposures, providing mechanistic insights that may help explain PFAS' long-term health effects.

METHODS: The study included 590 mother-infant dyads from the Boston Birth Cohort. We measured PFAS in maternal plasma samples collected 24-72 h after delivery and metabolites in cord plasma samples. We used metabolome-wide association studies and pathway enrichment analyses to identify metabolites and pathways associated with individual PFAS, and quantile-based g-computation models to examine associations of metabolites with the PFAS mixture. We used False Discovery Rate to account for multiple comparisons.

RESULTS: We found that 331 metabolites and 18 pathways were associated with ≥ 1 PFAS, and 38 metabolites were associated with the PFAS mixture, predominantly amino acids and lipids. Amino acids such as alanine and lysine and their pathways, crucial to energy generation, biosynthesis, and bone health, were associated with PFAS and may explain PFAS' effects on fetal growth restriction. Carnitines and carnitine shuttle pathway, associated with 7 PFAS and the PFAS mixture, are involved in mitochondrial fatty acid β-oxidation, which may predispose higher risks of fetal and child growth restriction and cardiovascular diseases. Lipids, such as glycerophospholipids and their related pathway, can contribute to insulin resistance and diabetes by modulating transporters on cell membranes, participating in β-cell signaling pathways, and inducing oxidative damage. Neurotransmission-related metabolites and pathways associated with PFAS, including cofactors, precursors, and neurotransmitters, may explain the PFAS' effects on child neurodevelopment. We observed stronger associations between prenatal PFAS exposures and metabolites in males.

CONCLUSIONS: This prospective birth cohort study contributes to the limited literature on potential metabolomic perturbations for prenatal PFAS exposures. Future studies are needed to replicate our findings and link prenatal PFAS associated metabolomic perturbations to long-term child health outcomes.

Demirel, Omer Burak, Fahime Ghanbari, Christopher W Hoeger, Connie W Tsao, Adele Carty, Long H Ngo, Patrick Pierce, et al. (2024) 2024. “Late Gadolinium Enhancement Cardiovascular Magnetic Resonance With Generative Artificial Intelligence.”. Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance 27 (1): 101127. https://doi.org/10.1016/j.jocmr.2024.101127.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction.

METHODS: A generative AI-based image enhancement was used to improve the sharpness of 2D LGE images acquired with low spatial resolution in the phase-encode direction. The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. The model was implemented inline, allowing the reconstruction of images on the scanner console. We prospectively enrolled 100 patients (55 ± 14 years, 72 males) referred for clinical CMR at 3T. We collected three sets of LGE images in each subject, with in-plane spatial resolutions of 1.5 × 1.5-3-6 mm2. The generative AI model enhanced in-plane resolution to 1.5 × 1.5 mm2 from the low-resolution counterparts. Images were compared using a blur metric, quantifying the perceived image sharpness (0 = sharpest, 1 = blurriest). LGE image sharpness (using a 5-point scale) was assessed by three independent readers.

RESULTS: The scan times for the three imaging sets were 15 ± 3, 9 ± 2, and 6 ± 1 s, with inline generative AI-based images reconstructed time of ∼37 ms. The generative AI-based model improved visual image sharpness, resulting in lower blur metric compared to low-resolution counterparts (AI-enhanced from 1.5 × 3 mm2 resolution: 0.3 ± 0.03 vs 0.35 ± 0.03, P < 0.01). Meanwhile, AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images showed similar blur metric (0.30 ± 0.03 vs 0.31 ± 0.03, P = 1.0) Additionally, there was an overall 18% improvement in image sharpness between AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images in the subjective blurriness score (P < 0.01).

CONCLUSION: The generative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis.

Lai, Michelle, Simon T Dillon, Xuesong Gu, Tina L Morhardt, Yuyan Xu, Noel Y Chan, Beibei Xiong, et al. (2024) 2024. “Serum Protein Risk Stratification Score for Diagnostic Evaluation of Metabolic Dysfunction-Associated Steatohepatitis.”. Hepatology Communications 8 (12). https://doi.org/10.1097/HC9.0000000000000586.

BACKGROUND: Reliable, noninvasive tools to diagnose at-risk metabolic dysfunction-associated steatohepatitis (MASH) are urgently needed to improve management. We developed a risk stratification score incorporating proteomics-derived serum markers with clinical variables to identify high-risk patients with MASH (NAFLD activity score >4 and fibrosis score >2).

METHODS: In this 3-phase proteomic study of biopsy-proven metabolic dysfunction-associated steatotic fatty liver disease, we first developed a multi-protein predictor for discriminating NAFLD activity score >4 based on SOMAscan proteomics quantifying 1305 serum proteins from 57 US patients. Four key predictor proteins were verified by ELISA in the expanded US cohort (N = 168) and enhanced by adding clinical variables to create the 9-feature MASH Dx score, which predicted MASH and also high-risk MASH (F2+). The MASH Dx score was validated in 2 independent, external cohorts from Germany (N = 139) and Brazil (N = 177).

RESULTS: The discovery phase identified a 6-protein classifier that achieved an AUC of 0.93 for identifying MASH. Significant elevation of 4 proteins (THBS2, GDF15, SELE, and IGFBP7) was verified by ELISA in the expanded discovery and independently in the 2 external cohorts. MASH Dx score incorporated these proteins with established MASH risk factors (age, body mass index, ALT, diabetes, and hypertension) to achieve good discrimination between MASH and metabolic dysfunction-associated steatotic fatty liver disease without MASH (AUC: 0.87-discovery; 0.83-pooled external validation cohorts), with similar performance when evaluating high-risk MASH F2-4 (vs. MASH F0-1 and metabolic dysfunction-associated steatotic fatty liver disease without MASH).

CONCLUSIONS: The MASH Dx score offers the first reliable noninvasive approach combining novel, biologically plausible ELISA-based fibrosis markers and clinical parameters to detect high-risk MASH in patient cohorts from the United States, Brazil, and Europe.

Ke, Janny X C, Tim T H Jen, Sihaoyu Gao, Long Ngo, Lang Wu, Alana M Flexman, Stephan K W Schwarz, Carl J Brown, and Matthias Görges. (2024) 2024. “Development and Internal Validation of Time-to-Event Risk Prediction Models for Major Medical Complications Within 30 Days After Elective Colectomy.”. PloS One 19 (12): e0314526. https://doi.org/10.1371/journal.pone.0314526.

BACKGROUND: Patients undergoing colectomy are at risk of numerous major complications. However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monitoring. We sought to develop and internally validate Cox proportional hazards models to predict time-to-complication of major complications within 30 days after elective colectomy.

METHODS: We studied a retrospective cohort from the multicentered American College of Surgeons National Surgical Quality Improvement Program procedure-targeted colectomy dataset. Patients aged 18 years or above, who underwent elective colectomy between January 1, 2014 and December 31, 2019 were included. A priori candidate predictors were selected based on variable availability, literature review, and multidisciplinary team consensus. Outcomes were mortality, hospital readmission, myocardial infarction, cerebral vascular events, pneumonia, venous thromboembolism, acute renal failure, and sepsis or septic shock within 30 days after surgery.

RESULTS: The cohort consisted of 132145 patients (mean ± SD age, 61 ± 15 years; 52% females). Complication rates ranged between 0.3% (n = 383) for cardiac arrest and acute renal failure to 5.3% (n = 6986) for bleeding requiring transfusion, with readmission rate of 8.6% (n = 11415). We observed distinct temporal patterns for each complication: the median [quartiles] postoperative day of complication diagnosis ranged from 1 [0, 2] days for bleeding requiring transfusion to 12 [6, 18] days for venous thromboembolism. Models for mortality, myocardial infarction, pneumonia, and renal failure showed good discrimination with a concordance > 0.8, while models for readmission, venous thromboembolism, and sepsis performed poorly with a concordance of 0.6 to 0.7. Models exhibited good calibration but ranges were limited to low probability areas.

CONCLUSIONS: We developed and internally validated time-to-event prediction models for complications after elective colectomy. Once further validated, the models can facilitate tailored monitoring of high risk patients during high risk periods.

TRIAL REGISTRATION: Clinicaltrials.gov (NCT05150548; Principal Investigator: Janny Xue Chen Ke, M.D., M.Sc., F.R.C.P.C.; initial posting: November 25, 2021).

Weissman, Gary E, Laura Zwaan, and Sigall K Bell. (2024) 2024. “Diagnostic Scope: The AI Can’t See What the Mind Doesn’t Know.”. Diagnosis (Berlin, Germany). https://doi.org/10.1515/dx-2024-0151.

BACKGROUND: Diagnostic scope is the range of diagnoses found in a clinical setting. Although the diagnostic scope is an essential feature of training and evaluating artificial intelligence (AI) systems to promote diagnostic excellence, its impact on AI systems and the diagnostic process remains under-explored.

CONTENT: We define the concept of diagnostic scope, discuss its nuanced role in building safe and effective AI-based diagnostic decision support systems, review current challenges to measurement and use, and highlight knowledge gaps for future research.

SUMMARY: The diagnostic scope parallels the differential diagnosis although the latter is at the level of an encounter and the former is at the level of a clinical setting. Therefore, diagnostic scope will vary by local characteristics including geography, population, and resources. The true, observed, and considered scope in each setting may also diverge, both posing challenges for clinicians, patients, and AI developers, while also highlighting opportunities to improve safety. Further work is needed to systematically define and measure diagnostic scope in terms that are accurate, equitable, and meaningful at the bedside. AI tools tailored to a particular setting, such as a primary care clinic or intensive care unit, will each require specifying and measuring the appropriate diagnostic scope.

OUTLOOK: AI tools will promote diagnostic excellence if they are aligned with patient and clinician needs and trained on an accurately measured diagnostic scope. A careful understanding and rigorous evaluation of the diagnostic scope in each clinical setting will promote optimal care through human-AI collaborations in the diagnostic process.

Glenn, Andrea J, Fenglei Wang, Anne-Julie Tessier, JoAnn E Manson, Eric B Rimm, Kenneth J Mukamal, Qi Sun, et al. (2024) 2024. “Dietary Plant-to-Animal Protein Ratio and Risk of Cardiovascular Disease in 3 Prospective Cohorts.”. The American Journal of Clinical Nutrition 120 (6): 1373-86. https://doi.org/10.1016/j.ajcnut.2024.09.006.

BACKGROUND: Dietary guidelines recommend substituting animal protein with plant protein, however, the ideal ratio of plant-to-animal protein (P:A) remains unknown.

OBJECTIVES: We aimed to evaluate associations between the P:A ratio and incident cardiovascular disease (CVD), coronary artery disease (CAD), and stroke in 3 cohorts.

METHODS: Multivariable-adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) for CVD outcomes among 70,918 females in the Nurses' Health Study (NHS) (1984-2016), 89,205 females in the NHSII (1991-2017) and 42,740 males from the Health Professionals Follow-up Study (1986-2016). The P:A ratio was based on percent energy from plant and animal protein and assessed using food frequency questionnaires every 4 y.

RESULTS: During 30 y of follow-up, 16,118 incident CVD cases occurred. In the pooled multivariable-adjusted models, participants had a lower risk of total CVD [HR: 0.81; 95% confidence interval (CI): 0.76, 0.87; P trend < 0.001], CAD (HR: 0.73; 95% CI: 0.67, 0.79; P trend < 0.001), but not stroke (HR: 0.98; 95% CI: 0.88, 1.09; P trend = 0.71), when comparing highest to lowest deciles of the P:A ratio (ratio: ∼0.76 compared with ∼0.24). Dose-response analyses showed evidence of linear and nonlinear relationships for CVD and CAD, with more marked risk reductions early in the dose-response curve. Lower risk of CVD (HR: 0.72; 95% CI: 0.64, 0.82) and CAD (HR: 0.64; 95% CI: 0.55, 0.75) were also observed with higher ratios and protein density (20.8% energy) combined. Substitution analyses indicated that replacing red and processed meat with several plant protein sources showed the greatest cardiovascular benefit.

CONCLUSIONS: In cohort studies of United States adults, a higher plant-to-animal protein ratio is associated with lower risks of CVD and CAD, but not stroke. Furthermore, a higher ratio combined with higher protein density showed the most cardiovascular benefit.

Lin, Pi-I Debby, Andres Cardenas, Lisa B Rokoff, Sheryl L Rifas-Shiman, Mingyu Zhang, Julianne Botelho, Antonia M Calafat, et al. (2024) 2024. “Associations of PFAS Concentrations During Pregnancy and Midlife With Bone Health in Midlife: Cross-Sectional and Prospective Findings from Project Viva.”. Environment International 194: 109177. https://doi.org/10.1016/j.envint.2024.109177.

BACKGROUND: PFAS may impair bone health, but effects of PFAS exposure assessed during pregnancy and the perimenopause-life stages marked by rapidly changing bone metabolism-on later life bone health are unknown.

METHODS: We studied 531 women in the Boston-area Project Viva cohort. We used multivariable linear, generalized additive, and mixture models to examine associations of plasma PFAS concentrations during early pregnancy [median (IQR) maternal age 32.9 (6.2) years] and midlife [age 51.2 (6.3)] with lumbar spine, total hip, and femoral neck areal bone mineral density (aBMD) and bone turnover biomarkersassessed in midlife. We examined effect modification by diet and physical activity measured at the time of PFAS exposure assessment and by menopausal status in midlife.

RESULTS: Participants had higher PFAS concentrations during pregnancy [1999-2000; e.g., PFOA median (IQR) 5.4 (3.8) ng/mL] than in midlife [2017-2021; e.g.

, PFOA: 1.5 (1.2) ng/mL]. Women with higher PFOA, PFOS and PFNA during pregnancy had higher midlife aBMD, especially of the spine [e.g., 0.28 (95% CI: 0.07, 0.48) higher spine aBMD T-score, per doubling of PFOA], with stronger associations observed among those with higher diet quality. In contrast, higher concentrations of all PFAS measured in midlife were associated with lower concurrent aBMD at all sites [e.g., -0.21 (-0.35, -0.07) lower spine aBMD T-score, per doubling of PFOA]; associations were stronger among those who were postmenopausal. The associations of several PFAS with bone resorption (loss) were also stronger among postmenopausal versus premenopausal women.

DISCUSSION: Plasma PFAS measured during pregnancy versus in midlife had different associations with midlife aBMD. We found an adverse association of PFAS measured in midlife with midlife aBMD, particularly among postmenopausal women. Future studies with longer follow-up are needed to elucidate the effect of PFAS on bone health during the peri- and postmenopausal years.