Search

Background: Iodinated contrast is commonly used for radiological procedures, with one dose delivering several hundred-fold the daily requirements needed for normal thyroid hormone production. Risks of excess iodine include incident thyroid dysfunction, which is associated with adverse cardiac outcomes, yet there are no prospective studies investigating the changes in cardiac physiology following iodine contrast administration. This study was conducted to investigate the longitudinal relationships between the amount of iodinated contrast administration and changes in cardiac electrophysiology and structure. Methods: A longitudinal cohort study was conducted with prospectively enrolled participants who received iodine contrast for elective computed tomography or coronary angiography. Serum thyroid function tests, electrocardiograms (EKG), and transthoracic echocardiograms were obtained serially until 36 months. Trends of electrical and structural cardiac changes following iodine contrast administration were assessed using mixed effect models. Results: The cohort was composed of 129 patients (median age, 70 [interquartile range: 63, 75] years; 98% male). Larger amounts of iodine exposure were associated with increases in QRS and QTc durations and decreased ejection fraction (EF), and these associations were still observed for follow-up EF after additionally adjusting for baseline values (the high-iodine contrast group vs. the low-iodine contrast group, −4.23% [confidence interval, −7.66% to −0.79%]). Dose–response analyses also showed lower EF with larger amounts of iodine received; these trends were not significant for the EKG parameters studied. Conclusions: Over a period of up to 36 months, a larger amount of administered iodine contrast was associated with lower EF among participants. Further investigation is needed to elucidate the long-term trends of electrical and structural cardiac function after iodine contrast administration. [ABSTRACT FROM AUTHOR]

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline. Expanding coverage to individuals with high predicted benefit generated greater reduction in depression prevalence than expanding to all eligible individuals (21.5 vs 8.8 percentage-point reduction; adjusted difference = +12.7 [95% CI, +4.6 to +20.8]; P  = 0.003), at substantially lower cost per case prevented ($16 627 vs $36 048; adjusted difference = −$18 598 [95% CI, −156 953 to −3120]; P  = 0.04). Medicaid coverage reduces depression substantially more in a subset of the population than others, in ways that are predictable in advance. Targeting coverage on those most likely to benefit could improve the effectiveness and efficiency of insurance expansion. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Aims In patients with advanced heart failure requiring dobutamine infusion, it is usually recommended to initiate beta-blockers after weaning from dobutamine. However, beta-blockers are sometimes initiated under dobutamine infusion in a real-world scenario. The association between such early beta-blocker initiation with clinical outcomes is unknown. Therefore, this study investigates the association between initiating beta-blockers under dobutamine infusion and survival outcomes. Methods and results This observational study with a multicentre inpatient-care database emulated a pragmatic randomized controlled trial (RCT) of the beta-blocker initiation strategy. First, 1151 patients on dobutamine and not on beta-blockers on the day of heart failure admission (Day 0) were identified. Among 1095 who met eligibility criteria, patients who were eventually initiated beta-blockers under dobutamine infusion by Day 7 (early initiation strategy) were 1:1 matched to those who were not initiated (conservative strategy). The methods of cloning, censoring, and weighting were applied to emulate the target trial. Patients were followed up for up to 30 days. The primary outcome was all-cause death. Among 780 matched patients (median age, 81 years), the adjusted hazard ratio was 1.11 (95% confidence interval 0.75–1.64, P = 0.59) for the early initiation strategy. The estimated 30-day all-cause mortalities in the early initiation strategy and the conservative strategy were 19.3% (10.6–30.7) and 16.2% (9.2–25.3), respectively. The results were consistent when we used different days to determine strategies (i.e. 5 and 9) instead of 7 days. Conclusion The present observational study emulating a pragmatic RCT found no positive or negative association between beta-blocker initiation under dobutamine infusion and overall survival. [ABSTRACT FROM AUTHOR]

Background Although thyroid eye disease (TED) can impact social and psychological well-being, the epidemiological evidence of TED is lacking in Japan. Methods Nationwide claims databases provided by JMDC Inc. and Medical Data Vision Co. Ltd. and national population statistics are used. Three TED definitions ranging from a strict definition only including a TED diagnosis to a broad definition including a TED diagnosis and considering ocular symptoms are considered. The proportion of patients by severity and disease activity are estimated based on definitions that would allow identification of those patients within the claims data. Results The incidence rate per 100 000 person-years ranged from 7.3 to 11.1 for the strict and broad TED definitions, respectively. For fiscal year 2020 (April 2020 to March 2021) the prevalence rate ranged between 24.65 (strict TED) and 37.58 (broad TED) per 100 000 persons. These correspond to 25 383 and 38 697 patients for the strict and broad TED definitions, respectively. Regardless of the definition used, a predominance of female patients was observed, and the highest burden of the disease was seen in the age group of 35 to 59. Mild and inactive forms of TED were predominant (about 85% and 74%, respectively). Conclusion The incidence and prevalence of TED in Japan were 7.3 to 11.1 per 100 000 person-years and 24.65 to 37.58 per 100 000 persons, respectively. The robust results of this database study add valuable real-world evidence on the incidence and prevalence of TED in Japan. [ABSTRACT FROM AUTHOR]

Competing Interests: Competing interests: None.

Objectives: To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A 1c (HbA 1c ) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly., Design: Secondary analysis of randomized controlled trial., Setting: Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured., Participants: 12 134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups., Interventions: Health insurance (Medicaid) coverage., Main Outcomes Measures: The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA 1c using a machine learning causal forest algorithm (with instrumental variables). Characteristics of individuals with positive predicted benefits of Medicaid coverage based on the algorithm were compared with the characteristics of others. The effect of Medicaid coverage was calculated on blood pressure and HbA 1c among individuals with high predicted benefits., Results: In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mm Hg and mean HbA 1c concentrations was 5.3% (standard deviation 0.6%). Our causal forest model showed heterogeneity in the effect of Medicaid coverage on systolic blood pressure and HbA 1c . Individuals with lower baseline healthcare charges, for example, had higher predicted benefits from gaining Medicaid coverage. Medicaid coverage significantly lowered systolic blood pressure (-4.96 mm Hg (95% confidence interval -7.80 to -2.48)) for people predicted to benefit highly. HbA 1c was also significantly reduced by Medicaid coverage for people with high predicted benefits, but the size was not clinically meaningful (-0.12% (-0.25% to -0.01%))., Conclusions: Although Medicaid coverage did not improve cardiovascular risk factors on average, substantial heterogeneity was noted in the effects within that population. Individuals with high predicted benefits were more likely to have no or low prior healthcare charges, for example. Our findings suggest that Medicaid coverage leads to improved cardiovascular risk factors for some, particularly for blood pressure, although those benefits may be diluted by individuals who did not experience benefits., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure and declare: support from the Japan Society for the Promotion of Science, the Japan Science and Technology Agency, National Institutes of Health, and Gregory Annenberg Weingarten, GRoW @ Annenberg for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Although polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level., Methods: We employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369,942) and BioBank Japan (BBJ; n = 149,421)., Results: Here we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman's ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention., Conclusions: Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine., (© 2024. The Author(s).)

Background: Estimating heterogeneous treatment effects (HTEs) in randomized controlled trials (RCTs) has received substantial attention recently. This has led to the development of several statistical and machine learning (ML) algorithms to assess HTEs through identifying individualized treatment effects. However, a comprehensive review of these algorithms is lacking. We thus aimed to catalog and outline currently available statistical and ML methods for identifying HTEs via effect modeling using clinical RCT data and summarize how they have been applied in practice., Study Design and Setting: We performed a scoping review using pre-specified search terms in MEDLINE and Embase, aiming to identify studies that assessed HTEs using advanced statistical and ML methods in RCT data published from 2010 to 2022., Results: Among a total of 32 studies identified in the review, 17 studies applied existing algorithms to RCT data, and 15 extended existing algorithms or proposed new algorithms. Applied algorithms included penalized regression, causal forest, Bayesian causal forest, and other meta-learner frameworks. Of these methods, causal forest was the most frequently used (7 studies) followed by Bayesian causal forest (4 studies). Most applications were in cardiology (6 studies), followed by psychiatry (4 studies). We provide example R codes to illustrate how to implement these algorithms., Conclusion: This review identified and outlined various algorithms currently used to identify HTEs and individualized treatment effects in RCT data. Given the increasing availability of new algorithms, analysts should carefully select them after examining model performance and considering how the models will be used in practice., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Observational studies have reported associations between primary aldosteronism (PA) and cardiovascular outcomes, including coronary artery diseases (CAD), congestive heart failure (CHF), and stroke. However, establishing causality remains a challenge due to the lack of randomized controlled trial data on this topic. We thus aimed to investigate the causal relationship between PA and the risk of developing CAD, CHF, and stroke., Methods and Results: Cross-ancestry meta-analysis of genome-wide association studies combining East Asian and European ancestry (1560 PA cases and 742 139 controls) was conducted to identify single-nucleotide variants that are associated with PA. Then, using the identified genetic variants as instrumental variables, we conducted the 2-sample Mendelian randomization analysis to investigate the causal relationship between PA and incident CAD, CHF, and stroke among both East Asian and European ancestry. Summary association results were extracted from large genome-wide association studies consortia. Our cross-ancestry meta-analysis of East Asian and European populations identified 7 genetic loci significantly associated with the risk of PA, for which the genes nearest to the lead variants were CASZ1 , WNT2B , HOTTIP , LSP1 , TBX3 , RXFP2 , and NDP . Among the East Asian population, the pooled odds ratio estimates using these 7 genetic instruments of PA were 1.07 (95% CI, 1.03-1.11) for CAD, 1.10 (95% CI, 1.01-1.20) for CHF, and 1.13 (95% CI, 1.09-1.18) for stroke. The results were consistent among the European population., Conclusions: Our 2-sample Mendelian randomization study revealed that PA had increased risks of CAD, CHF, and stroke. These findings highlight that early and active screening of PA is critical to prevent future cardiovascular events.

Agranulocytosis is a serious adverse effect of methimazole (MMI) and propylthiouracil (PTU), and although there have been reports suggesting a dose-dependent incidence in relation to both drugs, the evidence has not been conclusive. The objective of our study was to determine whether the incidences of agranulocytosis induced by MMI and PTU exhibit dose-dependency. The subjects were 27,784 patients with untreated Graves' disease, 22,993 of whom were on an antithyroid drug treatment regimen for more than 90 days. Within this subset, 18,259 patients had been treated with MMI, and 4,734 had been treated with PTU. The incidence of agranulocytosis according to dose in the MMI group was 0.13% at 10 mg/day, 0.20% at 15 mg/day, 0.32% at 20 mg/day, and 0.47% at 30 mg/day, revealing a significant dose-dependent increase. In the PTU group, there were 0 cases of agranulocytosis at doses of 125 mg/day and below, 0.33% at 150 mg/day, 0.31% at 200 mg/day, and 0.81% at 300 mg/day, also revealing a significant dose-dependent increase. The incidence of agranulocytosis at MMI 15 mg and PTU 300 mg, i.e., at the same potency in terms of hormone synthesis inhibition, was 0.20% and 0.81%, respectively, and significantly higher in the PTU group. Our findings confirm a dose-dependent increase in the incidence of agranulocytosis with both drugs, but that at comparable thyroid hormone synthesis inhibitory doses PTU has a considerably higher propensity to induce agranulocytosis than MMI does.

In observational studies, identifying and adjusting for a sufficient set of confounders is crucial for accurately estimating the causal effect of the exposure on the outcome. Even in studies with large sample sizes, which typically benefit from small variances in estimates, there is a risk of producing estimates that are precisely inaccurate if the study suffers from systematic errors or biases, including confounding bias. To date, several approaches have been developed for selecting confounders. In this article, we first summarize the epidemiological and statistical approaches to identify a sufficient set of confounders. Particularly, we introduce the modified disjunctive cause criterion as one of the most useful approaches, which involves controlling for any pre-exposure covariate that affects the exposure, outcome, or both. It then excludes instrumental variables but includes proxies for the shared common cause of exposure and outcome. Statistical confounder selection is also useful when dealing with a large number of covariates, even in studies with small sample sizes. After introducing several approaches, we discuss some pitfalls and considerations in confounder selection, such as the adjustment for instrumental variables, intermediate variables, and baseline outcome variables. Lastly, as it is often difficult to comprehensively measure key confounders, we introduce two statistics, E-value and Robustness value, for assessing sensitivity to unmeasured confounders. Illustrated examples are provided using the National Health and Nutritional Examination Survey Epidemiologic Follow-up Study. Integrating these principles and approaches will enhance our understanding of confounder selection and facilitate better reporting and interpretation of future epidemiological studies.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

Background: Although living alone versus with others is a key social element for cardiovascular prevention in diabetes, evidence is lacking about whether the benefit of intensive glycemic and blood pressure (BP) control differs by living arrangements. We thus aim to investigate heterogeneity in the joint effect of intensive glycemic and BP control on cardiovascular events by living arrangements among participants with diabetes., Methods and Results: This study included 4731 participants with diabetes in the ACCORD-BP (Action to Control Cardiovascular Risk in Diabetes-Blood Pressure) trial. They were randomized into 4 study arms, each with glycated hemoglobin target (intensive, <6.0% versus standard, 7.0-7.9%) and systolic BP target (intensive, <120 mm Hg versus standard <140 mm Hg). Cox proportional hazard models were used to estimate the joint effect of intensive glycemic and BP control on the composite cardiovascular outcome according to living arrangements. At a mean follow-up of 4.7 years, the cardiovascular outcome was observed in 445 (9.4%) participants. Among participants living with others, intensive treatment for both glycemia and BP showed decreased risk of cardiovascular events compared with standard treatment (hazard ratio [HR], 0.68 [95% CI, 0.51-0.92]). However, this association was not found among participants living alone (HR, 0.96 [95% CI, 0.58-1.59]). P for interaction between intensive glycemic and BP control was 0.53 among participants living with others and 0.009 among those living alone ( P value for 3-way interaction including living arrangements was 0.049)., Conclusions: We found benefits of combining intensive glycemic and BP control for cardiovascular outcomes among participants living with others but not among those living alone. Our study highlights the critical role of living arrangements in intensive care among patients with diabetes.

Iodine is a micronutrient that is required for thyroid hormone synthesis. The iodide cycle in thyroid hormone synthesis consists of a series of transport, oxidation, organification, and binding/coupling steps in thyroid follicular cells. Common sources of iodine include the consumption of an iodine-rich diet or iodine fortified foods, the administration of amiodarone, iodine-containing supplements, or iodinated contrast media, and other miscellaneous sources. Methods to assess population iodine status include the measurement of urinary iodine concentrations, blood thyroglobulin levels, prevalence of elevated neonatal TSH levels, and thyroid volume. Although excessive iodine intake or exposure is generally well tolerated, an acute iodine load may result in thyroid dysfunction (hypothyroidism or hyperthyroidism) in certain susceptible individuals due to the failure to escape from the Wolff-Chaikoff effect and to the Jod-Basedow phenomenon, respectively. In this review, we discuss the associations between excessive iodine intake or exposure, with particular focus on iodinated contrast media as a common source of excess iodine in healthcare settings, and risks of incident thyroid dysfunction. We also summarize the risks of iodine excess in vulnerable populations and review current guidelines regarding the screening and monitoring of iodinated contrast-induced thyroid dysfunction. Finally, we discuss the long-term potential nonthyroidal health risks associated with iodine excess and suggest the need for more data to define safe upper limits for iodine intake, particularly in high-risk populations., (Published by Oxford University Press on behalf of the Endocrine Society 2024.)

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges in clarifying the estimand and underlying assumptions. This article proposes expressing causal structures using graphical tools-directed acyclic graphs (DAGs) and single-world intervention graphs (SWIGs)-in the planning phase of a clinical trial. It presents five rules for selecting a sufficient set of adjustment variables on the basis of a diagram representing the clinical trial, along with three case studies of randomized and single-arm trials and a brief tutorial on DAG and SWIG. Through the case studies, DAGs appear effective in clarifying assumptions for identifying causal effects, although SWIGs should complement DAGs due to their limitations in the presence of intercurrent events in oncology research.

Context: Iodinated contrast media (ICM) is a common source of excess iodine in medical settings, given the common use of iodinated radiologic procedures., Objective: To determine the long-term risks of thyroid dysfunction following iodinated contrast administration in a prospective study., Design, Setting, Participants: A longitudinal cohort study was conducted of patients in the U.S. Veterans Affairs medical system who received ICM., Main Outcome Measures: Serum thyroid function, thyroid antibody, and inflammatory markers were measured at baseline. Thyroid function tests were repeated at 1 month, 3 months, and every 6 months thereafter until 36 months. Risk of thyroid dysfunction and longitudinal changes in thyroid hormone levels were assessed using mixed effect models., Results: There were 122 participants (median age, 70.0 [IQR 62.2-74.0] years; 98.4% male). At baseline, six subjects had subclinical thyroid dysfunction prior to ICM receipt. During median follow-up of 18 months, iodine-induced thyroid dysfunction was observed in 11.5% (14/122); six (4.9%) developed hyperthyroidism (including one with overt hyperthyroidism) and eight (6.6%) subclinical hypothyroidism. At last follow-up, ten of 20 subjects with thyroid dysfunction (14 new-onset cases and six with preexisting thyroid dysfunction) had persistent subclinical hyperthyroidism or hypothyroidism. There were also subtle changes in thyroid hormones observed longitudinally within the reference ranges in the overall cohort., Conclusions: There is a rare long-term risk of an excess iodine load on thyroid dysfunction even among individuals from an overall iodine-sufficient region, supporting the need for targeted monitoring following iodinated contrast administration., (Published by Oxford University Press on behalf of the Endocrine Society 2024.)