Exposure-response Relationship Research Articles

Association between ambient temperature and cause-specific mortality: An individual-level case-crossover study in Suzhou, China

The changing climate poses a growing challenge to the population health. The objective of this study was to assess the association between ambient temperature and cause-specific mortality in Suzhou. Based on the non-accidental mortality data collected during 2008–2022 in Suzhou, China, this study utilized an individual-level case-crossover design to evaluate the associations of temperature with cause-specific mortality. We applied a distributed lag nonlinear model with a maximum lag of 14 days to account for lag effects. Mortality risk due to extreme cold (<2.5th percentile) and extreme heat (>97.5th percentile) was analyzed. A total of 634,530 non-accidental deaths were analyzed in this study. An inverse J-shaped exposure-response relationship was observed between ambient temperature and non-accidental mortality, with the minimum mortality temperature (MMT) at 29.1℃. The relative risk (RR) of mortality associated with extreme cold (2.5th percentile) was 1.37 [95 % confidence interval (CI): 1.30, 1.44], higher than estimate of 1.09 (95 %CI: 1.07, 1.11) for extreme heat (97.5th percentile) relative to the MMT. Heat effect lasted for 2–3 days, while cold effect could persist for almost 14 days. Higher mortality risk estimates were observed for cardiorespiratory deaths compared to total deaths, with statistically significant between-group differences. Consequently, this study provides first-hand evidence on the associations between ambient temperatures and mortality risks from various causes, which could help local government and policy-makers in designing targeted strategies and public health measures against the menace of climate change.

Exposure to source-specific air pollution in residential areas and its association with dementia incidence: a cohort study in Northern Sweden

The aim of this study was to investigate the relationship between source-specific ambient particulate air pollution concentrations and the incidence of dementia. The study encompassed 70,057 participants from the Västerbotten intervention program cohort in Northern Sweden with a median age of 40 years at baseline. High-resolution dispersion models were employed to estimate source-specific particulate matter (PM) concentrations, such as PM10 and PM2.5 from traffic, exhaust, and biomass (mainly wood) burning, at the residential addresses of each participant. Cox regression models, adjusted for potential confounding factors, were used for the assessment. Over 884,847 person-years of follow-up, 409 incident dementia cases, identified through national registers, were observed. The study population’s average exposure to annual mean total PM10 and PM2.5 lag 1–5 years was 9.50 µg/m3 and 5.61 µg/m3, respectively. Increased risks were identified for PM10-Traffic (35% [95% CI 0–82%]) and PM2.5-Exhaust (33% [95% CI − 2 to 79%]) in the second exposure tertile for lag 1–5 years, although no such risks were observed in the third tertile. Interestingly, a negative association was observed between PM2.5-Wood burning and the risk of dementia. In summary, this register-based study did not conclusively establish a strong association between air pollution exposure and the incidence of dementia. While some evidence indicated elevated risks for PM10-Traffic and PM2.5-Exhaust, and conversely, a negative association for PM2.5-Wood burning, no clear exposure–response relationships were evident.

Future temperature-related mortality in the UK under climate change scenarios: Impact of population ageing and bias-corrected climate projections

BackgroundExposure to heat and cold poses a serious threat to human health. In the UK, hotter summers, milder winters and an ageing population will shift how populations experience temperature-related health burdens. Estimating future burdens can provide insights on the drivers of temperature-related health effects and removing biases in temperature projections is an essential step to generating these estimates, however, the impact of various methods of correction is not well examined. MethodsWe conducted a detailed health impact assessment by estimating mortality attributable to temperature at a baseline period (2007–2018) and in future decades (2030s, 2050s and 2070s). Epidemiological exposure-response relationships were derived for all England regions and UK countries, to quantify cold and heat risk, and temperature thresholds where mortality increases. UK climate projections 2018 (UKCP18)were bias-corrected using three techniques: correcting for mean bias (shift or SH), variability (bias-correction or BC) and extreme values (quantile mapping or QM). These were applied in the health impact assessment, alongside consideration of population ageing and growth to estimate future temperature-related mortality. FindingsIn the absence of adaptation and assuming a high-end emissions scenario (RCP8.5), annual UK temperature-related mortality is projected to increase, with substantial differences in raw vs. calibrated projections for heat-related mortality, but smaller differences for cold-related mortality. The BC approach gave an estimated 29 deaths per 100,000 in the 2070s, compared with 50 per 100,000 using uncorrected future temperatures. We also found population ageing may exert a bigger impact on future mortality totals than the impact from future increases in temperature alone. Estimating future health burdens associated with heat and cold is an important step towards equipping decision-makers to deliver suitable care to the changing population. Correcting inherent biases in temperature projections can improve the accuracy of projected health burdens to support health protection measures and long-term resilience planning.

Emergency room visits (ERVs) among occupational groups associated with ambient conditions in Taiwan.

This population-based study explored emergency room visits (ERVs) from all-causes, circulatory and respiratory diseases among different occupational groups in Taiwan associated with ambient average temperature. Daily area-age-sex specific ERVs records were obtained from the Taiwan's Ministry of Health and Welfare from 2009 to 2018. Distributed lag-nonlinear model (DLNM) was used to estimate the exposure-response relationships between daily average temperature and ERVs for all-causes, circulatory and respiratory diseases by occupational groups. Random-effects meta-analysis was used to pool the overall cumulative relative risk (RR) and 95% confidence interval (CI). The exposure-response curves showed ERVs of all-cause and respiratory diseases increased with rising temperature across all occupational groups. These effects were consistently stronger among younger (20-64 years old) and outdoor workers. In contrast, ERVs risk from circulatory diseases increased significantly during cold snaps, with a substantially higher risk for female workers. Interestingly, female workers, regardless of indoor or outdoor work, consistently showed a higher risk of respiratory ERVs during hot weather compared to males. Younger workers (20-64 years old) exhibited a higher risk of ERVs, likely due to job profiles with greater exposure to extreme temperatures. Notably, the highest risk of all-causes ERVs was observed in outdoor male laborers (union members), followed by farmers and private employees, with the lowest risk among indoor workers. Conversely, female indoor workers and female farmers faced the highest risk of respiratory ERVs. Again, female farmers with consistent outdoor exposure had the highest risk of circulatory ERVs during cold conditions. Our findings highlighted the complexity of temperature-related health risks associated with different occupational contexts. The population-level insights into vulnerable occupational groups could provide valuable comprehension for policymakers and healthcare practitioners.

SERENE ER Analysis Part 2 SERENE-UC: Exposure-response Analysis of Higher Versus Standard Adalimumab Dosing Regimens for Patients with Moderately to Severely Active Ulcerative Colitis.

SERENE UC (NCT02065622) evaluated whether a higher adalimumab induction regimen improved patients with ulcerative colitis (UC) response, but a flat dose-response relationship was found in the induction study. We investigated exposure-response (ER) relationships in induction and maintenance studies considering patients' baseline characteristics. Adalimumab exposures were simulated using the established population pharmacokinetic model. Multivariable logistic regressions were used to assess the efficacy endpoints (clinical remission, endoscopic remission, endoscopic improvement) at weeks 8 and 52. In the induction study, an increasing ER trend with heterogeneity between induction regimens was shown, suggesting average concentration (Cavg) had a significant impact on primary efficacy endpoints within each group. However, data were not described by a single ER curve. Using inverse effective clearance as the exposure metric described trends across induction regimens with a single curve. Patients with inherently lower effective adalimumab clearance responded better. The patient response rates at week 52 showed no heterogeneity. A short-term increase in adalimumab dose did not drive better responses for induction, and apparent ER relationships were better explained by patient-inherent lower clearance. Conversely, during maintenance up to week 52, increasing the concentration via dose translated to better responses more robustly. The ER findings for SERENE UC were consistent with SERENE CD.

Niraparib Population Pharmacokinetics and Exposure-Response Relationships in Patients With Newly Diagnosed Advanced Ovarian Cancer

PurposeNiraparib is a poly(adenosine diphosphate [ADP]-ribose) polymerase inhibitor approved for the maintenance treatment of advanced ovarian cancer (OC). Niraparib was originally approved in recurrent OC at a fixed starting dose (FSD) of 300 mg once daily (QD). This analysis characterized the population pharmacokinetics (PK) of niraparib and evaluated the relationships between exposure, efficacy, and safety to support clinical use of an individualized dosing strategy, in which the starting dose of niraparib was adjusted based on patient characteristics to improve the benefit-risk profile. MethodsA population PK model was developed by pooling data from four niraparib clinical trials (PN001 [n = 104], QUADRA [n = 455], NOVA [n = 403], and PRIMA [n = 480]) in patients with solid tumors, including OC. Exposure-response analyses were conducted to explore the relationships of niraparib exposure with progression-free survival (PFS) and adverse events in the PRIMA study. A multivariate logistic regression model was also developed to estimate the probability of grade ≥3 thrombocytopenia, using data from patients enrolled in PRIMA and NOVA. The impact of an individualized starting dose (ISD) regimen (200 mg QD in patients with body weight [BW] <77 kg or platelet count [PLT] <150,000/µL, or 300 mg QD in patients with BW ≥77 kg and PLT ≥150,000/µL) on systemic exposure, efficacy, and safety was assessed. FindingsNiraparib disposition was best described by a 3-compartment model with linear elimination. Key covariates included baseline creatinine clearance, BW, albumin, and age, all of which had minor effects on niraparib exposure. Comparable model-predicted exposure up to the time of disease progression/death or censoring in the 300-mg FSD and 200-/300-mg ISD groups was consistent with the lower rate of dose reduction in the ISD groups. No consistent niraparib exposure-response relationship was observed for efficacy in all PRIMA patients (first-line OC), and no statistically significant difference was seen in PFS curves for patients receiving a niraparib dose of 200 mg versus 300 mg. In the multivariate regression model, performed using combined data from PRIMA and NOVA, higher niraparib exposure (area under the concentration-time curve at steady-state [AUCss]), lower BW, and lower PLT were associated with an increased risk of grade ≥3 thrombocytopenia. ImplicationsPopulation PK and exposure-response analyses support use of an ISD to improve the safety profile of niraparib, including reducing the rate of grade ≥3 thrombocytopenia, without compromising efficacy. Clinical trial registrationClinicalTrials.gov, NCT01847274 (NOVA), NCT00749502 (PN001), NCT02655016 (PRIMA), NCT02354586 (QUADRA), www.clinicaltrials.gov.

Serum albumin, genetic susceptibility, and risk of venous thromboembolism

BackgroundPrevious research on the association between serum albumin (ALB) and venous thromboembolism (VTE) has produced inconclusive results. The polygenic risk score is constructed from a set of independent risk variants associated with a disorder, enabling the identification of a larger fraction of the population at comparable or greater disease risk. It is still unknown whether ALB and genetic factors jointly contribute to the incidence of VTE. ObjectivesThe present study aimed to explore ALB, genetic susceptibility, and the risk of VTE. MethodsThe present investigation was an analysis of prospectively collected data from UK Biobank, a population-based, longitudinal cohort. Cox proportional models were used to calculate hazard ratios and 95% CIs for VTE. The Kaplan–Meier curve was utilized to visualize the cumulative risk of VTE according to different serum ALB levels, and the restricted cubic spline model was leveraged to explore the exposure–response relationship among ALB levels and VTE risk. ResultsDuring median follow-up of 13.5 years, 11,502 cases with VTE were diagnosed among 417,113 participants in the UK Biobank. The lower ALB levels were associated with a higher risk for VTE. Individuals with both a high genetic risk and lowest ALB level had the highest risk of VTE (hazard ratio, 3.89; 95% CI, 3.41-4.43), compared with those with low genetic risk and highest ALB level. The positive joint effects of low ALB and polygenic risk score increased the risk of VTE in individuals with high genetic risk. This study excluded non-European patients and primarily focused on the European population, which may limit the generalizability of the findings. ConclusionLow serum ALB levels were linked to an increased risk of VTE, which was in accordance with a linear dose–response relationship. There was a positive additive effect of ALB and genetic susceptibility on the risk of VTE. ALB could serve as a biomarker for predicting the risk of VTE.

Enhancing the health benefits of air quality improvement: a comparative study across diverse scenarios.

In many studies, linear methods were used to calculate health benefits of air quality improvement, but the relationship between air pollutants and diseases may be complex and nonlinear. In addition, previous studies using reference number as average number of diseases may overestimate the health benefits. Therefore, the nonlinear model estimation and resetting of the reference number were very important. Hospital admission data for coronary heart disease (CHD), meteorological data, and air pollutant data of Zibo City from 2015 to 2019 were collected. The generalized additive model (GAM) was used to explore the association between air pollutants and hospital admission for CHD, and to evaluate the effects on health benefits under different reference number settings. A total of 21,105 hospitalized cases for CHD were reported in Zibo during the study period. The results of the GAM showed there was a log-linear exposure-response relationship between O3 and hospital admissions for CHD, with RR (relative risk) of 1.0143 (95% CI: 1.0047 ~ 1.0239). There were log-nonlinear exposure-response relationships between PM10, PM2.5, SO2, and hospital admissions for CHD. With the increase of pollutants concentrations, the risk for hospital admission showed a trend of increasing first and then decreasing. Compared with the average hospital admissions as the reference number, health benefits calculated by hospital admissions predicted by the GAM model yielded lower. Using the World Health Organization air quality guidelines as reference, attributable fractions of O3, PM10, and PM2.5 were 1.97% (95% CI: 0.63 ~ 3.40%), 11.82% (95% CI: 8.60 ~ 15.24%), and 11.82% (95% CI: 8.79 ~ 15.04%), respectively. When quantifying health benefits brought by improving air quality, corresponding calculation methods should first be determined according to the exposure-response relationships between air pollutants and outcomes. Then, applying the average hospital admissions as reference number may overestimate health benefits resulting from improved air quality.

Population pharmacokinetics of Ainuovirine and exposure-response analysis in human immunodeficiency virus-infected individuals.

Ainuovirine (ANV) is a new generation of non-nucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus (HIV) type 1 infection. This study aimed to evaluate the population pharmacokinetic (PopPK) profile and exposure-response relationship of ANV among people living with HIV. Plasma concentration-time data from phase 1 and phase 3 clinical trials of ANV were pooled for developing the PopPK model. Exposure estimates obtained from the final model were used in exposure-response analysis for virologic responses and safety responses. ANV exhibited a nonlinear pharmacokinetic profile, which was best described by a two-compartment model with first-order elimination. There were no significant covariates correlated to the pharmacokinetic parameters of ANV. The PopPK parameter estimate (relative standard error [%]) for clearance adjusted for bioavailability (CL/F) was 6.46 (15.00) L/h, and the clearance of ANV increased after multiple doses. The exposure-response model revealed no significant correlation between the virologic response (HIV-RNA <50 copies/mL) at 48 weeks and the exposure, but the incidence of adverse events increased with the increasing exposure ( P value of steady-state trough concentration and area under the steady-state curve were 0.0177 and 0.0141, respectively). Our PopPK model supported ANV 150 mg once daily as the recommended dose for people living with HIV, requiring no dose adjustment for the studied factors. Optimization of ANV dose may be warranted in clinical practice due to an increasing trend in adverse reactions with increasing exposure. Chinese Clinical Trial Registry https://www.chictr.org.cn (Nos. ChiCTR1800018022 and ChiCTR1800019041).

Assessing the role of socio-economic factors in shaping the temperature-mortality exposure-response relationship in China

Non-optimal temperatures significantly influence public health. However, the role of socio-economic factors in modulating health risks associated with non-optimal temperatures varies geographically and among different populations. Thus, the meteorological, air quality, health data, and socio-economic indicators were obtained from 23 districts in North and 48 districts in East China, respectively. Employing a two-stage meta-analysis, the exposure-response relationship was constructed for temperature against mortality from non-accidental causes, cardiovascular and cerebrovascular diseases, and respiratory illnesses. Furthermore, a non-linear spline regression was applied to assess the impact of socio-economic indicators on the exposure-response relationship and predicted future risks under various Shared Socioeconomic Pathways. The results revealed that the influence of socio-economic factors on the exposure-response curve showed heterogeneity in East China and North China. In North China, the shape of the exposure-response curve changed greatly under different socio-economic levels, while it remained similar in East China. In East China, the relative risk of heat and cold exposure was reduced in regions with high GDP, high levels of public finance, good medical services, and a low proportion of the elderly population. Specifically, the risk of non-accidental deaths due to heat shows a nearly linear negative correlation with per capita GDP in East China, with a decrease of the relative risk by 0.075 for every 10 thousand yuan increase in per capita GDP. Future projections indicate that population aging plays a decisive role in shaping the exposure-response curves. Although economic growth can reduce the risk of heat-related mortality, the combined effect of population aging and economic increase results in steeper exposure-response curves in both hot and cold temperature ranges in the future. In conclusion, although spatial variations in relative risk changes still exist, enhancing the adaptive capacity of populations can mitigate health risks associated with future climate change.

Health effect of multiple air pollutant mixture on sarcopenia among middle-aged and older adults in China

BackgroundAs the global aging process accelerates, the health challenges posed by sarcopenia among middle-aged and older adults are becoming increasingly prominent. However, the available evidence on the adverse effects of air pollution on sarcopenia is limited, particularly in the Western Pacific region. This study aimed to explore relationships of multiple air pollutants with sarcopenia and related biomarkers using the nationally representative database. MethodsTotally, 6585 participants aged over 45 years were enrolled from the China Health and Retirement Longitudinal Study (CHARLS) in 2011 and 3443 of them were followed up until 2015. Air pollutants were estimated from high-resolution satellite-based spatial-temporal models. In the cross-sectional analysis, we used generalized linear regression, unconditional logistic regression analytical and restricted cubic spline (RCS) methods to assess the single-exposure and non-linear effects of multiple air pollutants on sarcopenia and related surrogate biomarkers (serum creatinine and cystatin C). Several popular mixture analysis techniques such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g-computation (Qgcomp) were further used to examinate the combined effects of multiple air pollutants. Logistic regression was used to further analyze the longitudinal association between air pollution and sarcopenia. ResultsEach interquartile range increase in PM2.5, PM10 and NO2 was significantly associated with an increased risk of sarcopenia, with adjusted odds ratios (aORs) of 1.09 [95 % confidence interval (CI): 1.01, 1.20], 1.24 (95 % CI: 1.14, 1.35) and 1.18 (95 % CI: 1.08, 1.28), respectively. Our findings also showed that five air pollutants were significantly associated with the sarcopenia index. In addition, employing a mixture analysis approach, we confirmed significant combined effects of air pollution mixtures on sarcopenia risk and associated biomarkers, with PM10 and PM2.5 identified as major contributors to the combined effect. The results of the exposure-response (E-R) relationships, subgroup analysis, longitudinal analysis and sensitivity analysis all showed the unfavorable impact of air pollution on sarcopenia risk and related vulnerable populations. ConclusionsSingle-exposure and co-exposure to multiple air pollutants were positively associated with sarcopenia among middle-aged and older adults in China. Our study provided new evidence that air pollution mixture was significantly associated with sarcopenia related biomarkers.

Coarse particulate air pollution and mortality in a multidrug-resistant tuberculosis cohort

RationaleThe association between ambient coarse particulate matter (PM2.5–10) and mortality in multi-drug resistant tuberculosis (MDR-TB) patients has not yet been studied. The modifying effects of temperature and humidity on this association are completely unknown. ObjectivesTo evaluate the effects of long-term PM2.5–10 exposures, and their modifications by temperature and humidity on mortality among MDR-TB patients. MethodsA Chinese cohort of 3469 MDR-TB patients was followed up from diagnosis until death, loss to follow-up, or the study's end, averaging 2567 days per patient. PM2.5–10 concentrations were derived from the difference between PM10 and PM2.5. Cox proportional hazard models estimated hazard ratios (HRs) per 3.74 μg/m3 (interquartile range, IQR) exposure to PM2.5–10 and all-cause mortality for the full cohort and individuals at distinct long-term and short-term temperature and humidity levels, adjusting for other air pollutants and potential covariates. Exposure-response relationships were quantified using smoothed splines. ResultsHazard ratios of 1.733 (95% CI, 1.407, 2.135) and 1.427 (1.114, 1.827) were observed for mortality in association with PM2.5–10 exposures for the full cohort under both long-term and short-term exposures to temperature and humidity. Modifying effects by temperature and humidity were heterogenous across sexes, age, treatment history, and surrounding environment measured by greenness and nighttime light levels. Nonlinear exposure-response curves suggestes a cumulative risk of PM2.5–10-related mortality starting from a low exposure concentration around 15 μg/m3. ConclusionLong-term exposure to PM2.5–10 poses significant harm among MDR-TB patients, with effects modified by temperature and humidity. Immediate surveillance of PM2.5–10 is crucial to mitigate the progression of MDR-TB severity, particularly due to co-exposures to air pollution and adverse weather conditions.

Association between Combined Metals and PFAS Exposure with Dietary Patterns: A Preliminary Study.

The global burden of chronic diseases has been increasing, with evidence suggesting that diet and exposure to environmental pollutants, such as per- and polyfluoroalkyl substances (PFAS) and heavy metals, may contribute to their development. The Dietary Inflammatory Index (DII) assesses the inflammatory potential of an individual's diet. However, the complex interplay between PFAS, heavy metals, and DII remains largely unexplored. The goal of this cross-sectional study was to investigate the associations between diet operationalized as the DII with individual and combined lead, cadmium, mercury, perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) exposures using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018. Descriptive statistics, a correlational analysis, and linear regression were initially used to assess the relationship between the variables of interest. We subsequently employed Bayesian kernel Machine regression (BKMR) to analyze the data to assess the non-linear, non-additive, exposure-response relationships and interactions between PFAS and metals with the DII. The multi-variable linear regression revealed significant associations between the DII and cadmium and mercury. Our BKMR analysis revealed a complex relationship between PFAS, metal exposures, and the DII. In our univariate exposure-response function plot, cadmium and mercury exhibited a positive and negative linear relationship, respectively, which indicated a positive and negative relationship across the spectrum of exposures with the DII. In addition, the bivariate exposure-response function between two exposures in a mixture revealed that cadmium had a robust positive relationship with the DII for different quantiles of lead, mercury, PFOA, and PFOS, indicating that increasing levels of cadmium are associated with the DII. Mercury's bivariate plot demonstrated a negative relationship across all quantiles for all pollutants. Furthermore, the posterior inclusion probability (PIP) results highlighted the consistent importance of cadmium and mercury with the inflammatory potential of an individual's diet, operationalized as the DII in our study, with both showing a PIP of 1.000. This was followed by PFOS with a PIP of 0.8524, PFOA at 0.5924, and lead, which had the lowest impact among the five environmental pollutants, with a PIP of 0.5596. Our study suggests that exposures to environmental metals and PFAS, particularly mercury and cadmium, are associated with DII. These findings also provide evidence of the intricate relationships between PFAS, heavy metals, and the DII. The findings underscore the importance of considering the cumulative effects of multi-pollutant exposures. Future research should focus on elucidating the mechanistic pathways and dose-response relationships underlying these associations in a study that examines causality, which will enable a deeper understanding of the dietary risks associated with environmental pollutants.

Association between exposure to organophosphorus pesticide and suicidal ideation among U.S. adults: A population-based study

ObjectiveThis study aims to investigate the potential link between exposure to organophosphorus pesticides (OPPs) and suicidal ideation (SI) among adults. MethodsThis study encompassed four cycles of the National Health and Nutrition Examination Survey (NHANES), involving 5244 participants aged 20 and above. SI was assessed using the Patient Health Questionnaire-9. The levels of exposure to OPPs were estimated by analyzing concentrations of OPP metabolites in urine samples. Multivariate logistic regression models were used to explore the association between exposure to each OPP and SI. Stratified analyses and interaction tests were conducted across various groups, including pairwise combinations of gender and age, as well as body mass index, smoking status, hypertension, and diabetes. Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR) models were applied to assess the cumulative impact of exposure to the four OPPs on SI, along with their respective contributions. Additionally, the potential interactions among these four OPPs were evaluated. ResultsMultivariate logistic regression revealed that only dimethylthiophosphate (DMTP) among OPPs demonstrated a statistically significant positive association with SI [OR: 1.18; 95 % CI: 1.02–1.37]. Stratified analyses indicated that the influence of OPPs on SI was particularly pronounced in young and older men. The WQS regression analysis revealed a statistically significant association between the mixed metabolites of OPPs and SI [OR = 1.10, 95 % CI: 1.04–1.16], with DMTP (weighted 0.63) contributing the most. Furthermore, the BKMR model supported a positive trend in the overall impact of these OPP metabolites on SI, displaying notable individual exposure-response relationships for DMTP (PIP: 0.77). ConclusionsOur study suggests an association between exposure to DMTP and an increased risk of SI. Specifically, young adult males and older males appear particularly susceptible to the effects of OPP exposure.

Population pharmacokinetics of TLD-1, a novel liposomal doxorubicin, in a phase I trial.

TLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1's population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling. The PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored. Medians standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0-∞ were 0.342 0.134mg/L and 40.1 18.9mg·h/L, respectively. The median half-life (95h) was 23.5h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate. The population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies. ClinicalTrials.gov-NCT03387917-January 2, 2018.

Time-varying brentuximab vedotin pharmacokinetics and weight-based dosing in paediatric patients despite lower exposure in those aged 2 to

We studied the pharmacokinetics and exposure-response relationships of the brentuximab vedotin (BV) antibody-drug conjugate (ADC) and unconjugated monomethyl auristatin E in haematologic malignancies. This population pharmacokinetic analysis included data from five adult and three paediatric studies. Exposures in virtual adult and paediatric populations following BV 1.8 mg/kg (maximum 180 mg) intravenously every 3 weeks were simulated. Clinical endpoints included overall response rate, grade ≥2 peripheral neuropathy (PN) and grade ≥3 neutropenia. BV ADC exhibited linear pharmacokinetics, well-described by a three-compartment model, with body weight being the only significant covariate for exposure. Monomethyl auristatin E exhibited time-varying formation rate. Simulated steady-state BV ADC exposures in patients aged 12 to <18 years were similar to those of adult patients, but 23%-38% lower in patients aged 2 to <12 years. Despite lower exposure, clinical activity was observed with BV 1.8 mg/kg every 3 weeks in those aged 2 to <12 years (overall response rate: 2 to <12 years, 60%; 12 to <18 years, 43%). In adult, but not paediatric patients, increased BV ADC exposures were associated with grade ≥2 PN and grade ≥3 neutropenia occurrence. BV pharmacokinetics in adult and paediatric patients were consistent. BV ADC exposures were lower in patients aged 2 to <12 years vs. ≥12 years, but no apparent clinically relevant differences in efficacy, grade ≥2 PN or grade ≥3 neutropenia were observed. These data support body weight-based dosing of BV in patients irrespective of age; thus, dose adjustment in those 2 to <12 years does not appear warranted.

Population Pharmacokinetic and Exposure-Response Modeling to Inform Risankizumab Dose Selection in Patients With Ulcerative Colitis.

Data from phase IIb/III and phase III studies were used to characterize the population pharmacokinetics of risankizumab and its exposure-response relationships for efficacy and safety in ulcerative colitis (UC) patients. A two-compartment model with first-order absorption and elimination accurately described risankizumab pharmacokinetics. Although certain covariates, namely, body weight, serum albumin, fecal calprotectin, sex, corticosteroid use, advanced therapy inadequate response, and pancolitis, were statistically correlated with risankizumab clearance, their impact on exposure was not clinically meaningful for efficacy or safety. Phase II exposure-response analyses demonstrated that the 1,200 mg intravenous (IV) induction dose at Weeks 0, 4, and 8 achieved near maximal response for all efficacy end points, with suboptimal efficacy from the 600 mg and little added benefit from the 1,800 mg regimens, justifying 1,200 mg IV as the induction dose in the phase III study. Phase III exposure-response analyses for efficacy during induction showed statistically significant exposure-response relationships at Week 12 following 1,200 mg IV at Weeks 0, 4, and 8, in line with phase IIb results. Exposure-response analyses for maintenance demonstrated modest improvement in Week 52 efficacy when increasing the subcutaneous dose from 180 mg to 360 mg with largely overlapping confidence intervals. Exposure-response analyses for safety indicated no apparent exposure-dependent safety events over the induction or maintenance treatment. Based on these results, the recommended dosing regimen for risankizumab in UC patients is 1,200 mg IV at Weeks 0, 4, and 8, followed by 180 mg or 360 mg subcutaneously at Week 12 and every 8 weeks thereafter.

Scaling features in high-concentrations PM2.5 evolution: the Ignored factor affecting scarlet fever incidence.

As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM2.5 (HC-PM2.5) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM2.5. It has been found that index D can quantify the time distribution of HC-PM2.5, and lower D values indicate more cluster distribution of HC-PM2.5. Moreover, scale-invariance in HC-PM2.5 at different time scales has been discovered, which indicates that HC-PM2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM2.5. This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM2.5 evolution.

Association of ambient temperature with tuberculosis incidence in Japan: An ecological study

ObjectivesAlthough several studies have investigated the effects of temperature on the incidence of tuberculosis (TB) in a single city or region, few studies have investigated the variations in this association using nationwide data. This study aimed to quantify the association between temporal variations in TB incidence and temperature across Japan. MethodsThe data on the weekly number of newly confirmed TB cases and meteorological variables in 47 Japanese prefectures from 2007 to 2019 were collected. The exposure-response relationships between TB incidence and temperature were quantified using a distributed lag nonlinear model for each prefecture, and estimates from all prefectures were then pooled using a meta-regression model to derive nationwide average associations. ResultsThis study included 335,060 patients with TB. Compared to those with minimum risk temperature on TB incidence (10th percentile at 4.45°C), people who were exposed to the highest temperature concentrations had a 52.0% (relative risk 1.52, 95% confidence interval 1.04-2.23) higher risk for TB incidence at the 99th percentile (30.1°C). Our results also emphasized the heterogeneity of these associations in different prefectures. Conclusions: Strengthening monitoring and public health strategies aimed at controlling temperature-related TB may be more effective when tailored to region-specific meteorological conditions.

Association of residential greenness with incident pneumonia: A prospective cohort study

Residential greenness is considered beneficial to human health, and its association with respiratory function has been found in previous studies. However, its link with pneumonia remains unclear. To explore the association of residential greenness with incident pneumonia, we conducted a prospective cohort study based on participants of the UK Biobank, followed from 2006 to 2010 to the end of 2019. Residential greenness was measured by Normalized Difference Vegetation Index (NDVI) within 500 m and 1000 m buffer. Cox proportional hazard models were conducted to assess the association, and restricted cubic spline models were also constructed to estimate their exposure-response relationship. Results demonstrate that residential greenness was negatively related to the risk of incident pneumonia. An interquartile (IQR) increase in NDVI 500-m buffer was associated with 4 % [HR (95 % CI) =0.96 (0.94, 0.97), P < 0.001] lower risk of incident pneumonia. Compared to the lowest greenness quartile (Q1), the highest quartile (Q4) had a lower risk of incident pneumonia, with the HR (95 % CI) estimated to be 0.91 (0.87, 0.95) (P values <0.001). Analyses based on NDVI 1000-m buffer obtained similar results. Furthermore, a significant effect of modifications by age and income on the linkage between residential greenness and incident pneumonia was found. These findings propose a potential effective prevention of incident pneumonia and provide the scientific basis for promoting the construction of residential greenness.