Amerindian ancestry proportion as a risk factor for inflammatory bowel diseases: results from a Latin American Andean cohort

Background and aims

Latin American populations remain underrepresented in genetic studies of inflammatory bowel diseases (IBDs). Most genetic association studies of IBD rely on Caucasian, African, and Asian individuals. These associations have yet to be evaluated in detail in the Andean region of South America. We explored the contribution of IBD-reported genetic risk variants to a Chilean cohort and the ancestry contribution to IBD in this cohort.

Methods

A total of 192 Chilean IBD patients were genotyped using Illumina's Global Screening Array. Genotype data were combined with similar information from 3,147 Chilean controls. The proportions of Aymara, African, European, and Mapuche ancestries were estimated using the software ADMIXTURE. We calculated the odds ratios (ORs) and 95% confidence intervals (CIs) for gender, age, and ancestry proportions. We also explored associations with previously reported IBD-risk variants independently and in conjunction with genetic ancestry.

Results

The first and third quartiles of the proportion of Mapuche ancestry in IBD patients were 24.7 and 34.2%, respectively, and the corresponding OR was 2.30 (95%CI 1.52-3.48) for the lowest vs. the highest group. Only one variant (rs7210086) of the 180 reported IBD-risk SNPs was associated with IBD risk in the Chilean cohort (adjusted P = 0.01). This variant is related to myeloid cells.

Conclusion

The type and proportion of Native American ancestry in Chileans seem to be associated with IBD risk. Variants associated with IBD risk in this Andean region were related to myeloid cells and the innate immune response.

Ethnicity influences phenotype and clinical outcomes: Comparing a South American with a North American inflammatory bowel disease cohort

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn disease (CD), has emerged as a global disease with an increasing incidence in developing and newly industrialized regions such as South America. This global rise offers the opportunity to explore the differences and similarities in disease presentation and outcomes across different genetic backgrounds and geographic locations. Our study includes 265 IBD patients. We performed an exploratory analysis of the databases of Chilean and North American IBD patients to compare the clinical phenotypes between the cohorts. We employed an unsupervised machine-learning approach using principal component analysis, uniform manifold approximation, and projection, among others, for each disease. Finally, we predicted the cohort (North American vs Chilean) using a random forest. Several unsupervised machine learning methods have separated the 2 main groups, supporting the differences between North American and Chilean patients with each disease. The variables that explained the loadings of the clinical metadata on the principal components were related to the therapies and disease extension/location at diagnosis. Our random forest models were trained for cohort classification based on clinical characteristics, obtaining high accuracy (0.86 = UC; 0.79 = CD). Similarly, variables related to therapy and disease extension/location had a high Gini index. Similarly, univariate analysis showed a later CD age at diagnosis in Chilean IBD patients (37 vs 24; P = .005). Our study suggests a clinical difference between North American and Chilean IBD patients: later CD age at diagnosis with a predominantly less aggressive phenotype (39% vs 54% B1) and more limited disease, despite fewer biological therapies being used in Chile for both diseases.

Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables

Pro-environmental behaviors towards climate change can be measured and evaluated in different fields. Typically, surveys are the standard tool for extracting personal information regarding this phenomenon. However, statistical modeling for these surveys is not straightforward, as the response variable is often not explicit. Hence, we propose a set of methodological procedures to deal with pro-environmental behavior data. First, validity evidence through a factorial analysis. Second, indexes are created from factor scores, where one of the latent factors summarizes a target variable. Third, a Beta regression is used to model the index of interest. Fourth, the inferential process is performed from a Bayesian perspective, in which posterior probabilities are used to sort and select the relevant variables. Finally, suitable models are obtained, and conclusions can be drawn from them. As a motivation, we used data from two Chilean surveys to illustrate our methodology as well as interpret and discuss the results.

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.

A tractable Bayesian joint model for longitudinal and survival data

We introduce a numerically tractable formulation of Bayesian joint models for longitudinal and survival data. The longitudinal process is modeled using generalized linear mixed models, while the survival process is modeled using a parametric general hazard structure. The two processes are linked by sharing fixed and random effects, separating the effects that play a role at the time scale from those that affect the hazard scale. This strategy allows for the inclusion of nonlinear and time-dependent effects while avoiding the need for numerical integration, which facilitates the implementation of the proposed joint model. We explore the use of flexible parametric distributions for modeling the baseline hazard function which can capture the basic shapes of interest in practice. We discuss prior elicitation based on the interpretation of the parameters. We present an extensive simulation study, where we analyze the inferential properties of the proposed models, and illustrate the trade-off between flexibility, sample size, and censoring. We also apply our proposal to two real data applications in order to demonstrate the adaptability of our formulation both in univariate time-to-event data and in a competing risks framework. The methodology is implemented in rstan.

A strategy to impute age at onset of a particular condition from external sources

A key hypothesis in epidemiological studies is that time to disease exposure provides relevant information to be considered in statistical models. However, the initiation time of a particular condition is usually unknown. Therefore, we developed a multiple imputation methodology for the age at onset of a particular condition, which is supported by incidence data from different sources of information. We introduced and illustrated such a methodology using simulated data in order to examine the performance of our proposal. Then, we analyzed the association of gallstones and fatty liver disease in the Maule Cohort, a Chilean study of chronic diseases, using participants' risk factors and six sources of information for the imputation of the age-occurrence of gallstones. Simulated studies showed that an increase in the proportion of imputed data does not affect the quality of the estimated coefficients associated with fully observed variables, while the imputed variable slowly reduces its effect. For the Chilean study, the categorized exposure time to gallstones is a significant variable, in which participants who had short and long exposure have, respectively, 26.2% and 29.1% higher chance of getting a fatty liver disease than non-exposed ones. In conclusion, our multiple imputation approach proved to be quite robust both in the linear/logistic regression simulation studies and in the real application, showing the great potential of this methodology.

Bayesian survival analysis with BUGS

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages is also discussed.

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.

Relationship between marital status and incidence of type 2 diabetes mellitus in a Brazilian rural population: The Baependi Heart Study

Many factors influence the incidence of type 2 diabetes mellitus (T2DM). Here, we investigated the associations between socio-demographic characteristics and familial history with the 5-year incidence of T2DM in a family-based study conducted in Brazil. T2DM was defined as baseline fasting blood glucose ≥ 126 mg/dL or the use of any hypoglycaemic drug. We excluded individuals with T2DM at baseline or if they did not attend two examination cycles. After exclusions, we evaluated a sample of 1,125 participants, part of the Baependi Heart Study (BHS). Mixed-effects logistic regression models were used to assess T2DM incident given different characteristics. At the 5-year follow-up, the incidence of T2DM was 6.7% (7.2% men and 6.3% women). After adjusting for age, sex, and education status, the model that combined marital and occupation status, skin color, and familial history of T2DM provided the best prediction for T2DM incidence. Only marital status was independently associated with T2DM incidence. Individuals that remained married, despite having significantly increased their weight, were significantly less likely to develop diabetes than their divorced counterparts.

Association between ambient beta particle radioactivity and lower hemoglobin concentrations in a cohort of elderly men

Although ionizing radiation is known to have detrimental effects on red blood cells, the effect of environmental radioactivity associated with ambient particulate matter (PM) is unknown. We hypothesized that exposure to ambient PM-associated beta particle radioactivity (PR_β) would be associated with a lower hemoglobin concentration. We studied 1.704 participants from the Normative Aging Study (NAS) over 36 years (1981-2017) who lived in Eastern, MA and the surrounding area. Exposures to PR_β was assessed using USEPA's RadNet monitoring network that measures gross beta radiation associated with ambient PM. Mixed effect models with a random intercept adjusting for potential confounders was used, including ambient black carbon (BC) and particulate matter ≤2.5 μm (PM_2.5) concentrations. Greater cumulative PR_β activities at 7-, 14-, 21- and 28-days before the hemoglobin determination were associated with lower hemoglobin concentrations. The greatest effect was for a 28-day moving average. An IQR of 0.83 × 10^-4 Bq/m³ of ambient PR_β was associated with a 0.12 g/dL decrease in hemoglobin concentration (95%CI: -0.18 to -0.05). The effects of PR_β were similar when the models were adjusted for ambient BC or PM_2.5. This is the first study to demonstrate an association between environmental ionizing radiation released from particulate matter with a lower hemoglobin concentration, suggesting that ambient radiation may contribute to the development of anemia.

Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each update of the full Bayesian inferential process. Our proposal is very general and can be easily applied to most popular joint models approaches. We illustrate the use of the presented sequential methodology in a joint model with competing risk events for a real scenario involving patients on mechanical ventilation in intensive care units (ICUs).

Cardiometabolic risk factors correlated with the incidence of dysglycaemia in a Brazilian normoglycaemic sample: the Baependi Heart Study cohort

BACKGROUND

Dysglycaemia is defined by elevated glucose levels in the blood, commonly characterized by impaired fasting glucose, impaired glucose tolerance, elevated glycated haemoglobin, or diabetes mellitus (DM) diagnosis. The abnormal levels of glucose may occur many years before DM, a condition known as prediabetes, which is correlated with comorbidities such as cardiovascular diseases. Therefore, the aim of this study was to investigate the incidence of prediabetic dysglycaemia and its relationship with cardiometabolic risk factors at a 5-year follow-up, based on an initially normoglycaemic sample in the Baependi Heart Study cohort.

METHODS

The data used comes from the Baependi Heart Study cohort, which consists of two periods: cycle 1 (2005-2006) and cycle 2 (2010-2013). For this study, we excluded those who had fasting blood glucose ≥ 100 mg/dL or were taking anti-diabetic medications at baseline, and those that had diabetes diagnosed in cycle 2. Mixed-effects logistic regression models were used to assess the association between cardiometabolic risk factors and the incidence of dysglycaemia, including a familiar random effect such as a cluster.

RESULTS

The incidence of prediabetic dysglycaemia was 12.8%, and it did not differ between men and women (14.4% and 11.6%, respectively). Two models were analysed to investigate the relationship between cardiometabolic risk factors and the occurrence of prediabetic dysglycaemia. The model that better explained the occurrence of dysglycaemia over the 5 years, after correction, included the waist circumference (WC) (measures and Δ), systolic blood pressure (SBP), HDL-c levels, and age. Although sex was not associated with the incidence of dysglycaemia, women and men showed differences in cardiometabolic risk factors related to glucose impairment: men who developed dysglycaemia showed, in parallel, higher LDL-c levels, TC/HDL-c ratio and DBP measurements; while these parameters remained similar between women who developed dysglycaemia and dysglycaemia-free women, after 5 years.

CONCLUSIONS

In an initially normoglycaemic sample of a highly mixed population living in a traditional Brazilian lifestyle, important cardiometabolic risk factors were associated with the occurrence of prediabetic dysglycaemia, and this relationship appeared to be more important in men. These results provide important insights about cardiovascular risk in prediabetic individuals.

Geomagnetic disturbances driven by solar activity enhance total and cardiovascular mortality risk in 263 U.S. cities

BACKGROUND

Short-term geomagnetic disturbances (GMD) driven by the quasi-periodic 11-year cycle of solar activity have been linked to a broad range of adverse health effects, including cardiovascular diseases (CVD) and total deaths. We conducted a large epidemiological study in 263 U.S. cities to assess the effects of GMD on daily deaths of total, CVD, myocardial infarction (MI), and stroke.

METHODS

We employed a two-step meta-analysis approach, in which we estimated city-specific and season-stratified mortality risk associated with a GMD parameter (Kp index) in 263 U.S. cities. In addition, sensitivity analysis was performed to assess whether effect modification of particulate matter (PM2.5) in the prior day changed Kp index effects on daily deaths after adjusting for confounders.

RESULTS

We found significant association between daily GMD and total, CVD, and MI deaths. The effects were even stronger when we adjusted the models for 24-h PM2.5 for different seasons. For example, in the winter and fall one standard deviation of z-score Kp index increase was associated with a 0.13 and 0.31% increase in total deaths, respectively (Winter: p = 0.01, 95% CI: 0.02 to 0.24; Fall: p = 0.00001; 95% CI: 0.23 to 0.4), without adjusting for PM2.5. The effects of GMD on total deaths were also observed in spring and summer in the models without PM2.5 (p = 0.00001). When the models were adjusted for PM2.5 the total deaths increased 0.47% in winter (p = 0.00001, 95% CI: 0.3 to 0.65) and by 0.23% in fall (p = 0.001, 95% CI: 0.09 to 0.37). The effects of GMD were also significant associated with MI deaths and CVD. No positive significant association were found between Kp and stroke. The GMD effects on deaths were higher than for 24 h-PM2.5 alone, especially in spring and fall.

CONCLUSION

Our results suggest that GMD is associated with total, CVD and MI deaths in 263 U. S cities. Increased mortality in the general population during GMD should be further investigated to determine whether those human physiological dynamics driven by variations in solar activity can be related to daily clinical cardiovascular observations.

SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

S. Typhimurium virulence changes caused by exposure to different non-thermal preservation treatments using C. elegans

The aims of this research study were: (i) to postulate Caenorhabditis elegans (C. elegans) as a useful organism to describe infection by Salmonella enterica serovar Typhimurium (S. Typhimurium), and (ii) to evaluate changes in virulence of S. Typhimurium when subjected repetitively to different antimicrobial treatments. Specifically, cauliflower by-product infusion, High Hydrostatic Pressure (HHP), and Pulsed Electric Fields (PEF). This study was carried out by feeding C. elegans with different microbial populations: E. coli OP50 (optimal conditions), untreated S. Typhimurium, S. Typhimurium treated once and three times with cauliflower by-product infusion, S. Typhimurium treated once and four times with HHP and S. Typhimurium treated once and four times with PEF. Bayesian survival analysis was applied to estimate C. elegans lifespan when fed with the different microbial populations considered. Results showed that C. elegans is a useful organism to describe infection by S. Typhimurium because its lifespan was reduced when it was infected. In addition, the application of antimicrobial treatments repetitively generated different responses: when cauliflower by-product infusion and PEF treatment were applied repetitively the virulence of S. Typhimurium was lower than when the treatment was applied once. In contrast, when HHP treatment was applied repetitively, the virulence of S. Typhimurium was higher than when it was applied once. Nevertheless, in all the populations analyzed treated S. Typhimurium had lower virulence than untreated S. Typhimurium.

Bayesian joint modeling of bivariate longitudinal and competing risks data: An application to study patient-ventilator asynchronies in critical care patients

Mechanical ventilation is a common procedure of life support in intensive care. Patient-ventilator asynchronies (PVAs) occur when the timing of the ventilator cycle is not simultaneous with the timing of the patient respiratory cycle. The association between severity markers and the events death or alive discharge has been acknowledged before, however, little is known about the addition of PVAs data to the analyses. We used an index of asynchronies (AI) to measure PVAs and the SOFA (sequential organ failure assessment) score to assess overall severity. To investigate the added value of including the AI, we propose a Bayesian joint model of bivariate longitudinal and competing risks data. The longitudinal process includes a mixed effects model for the SOFA score and a mixed effects beta regression model for the AI. The survival process is defined in terms of a cause-specific hazards model for the competing risks death or alive discharge. Our model indicates that the SOFA score is strongly related to vital status. PVAs are positively associated with alive discharge but there is not enough evidence that PVAs provide a more accurate indication of death prognosis than the SOFA score alone.

Pegfilgrastim can be effectively administered the same day as chemotherapy to prevent neutropenia-related complications

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Background: Pegfilgrastim has been proven to reduce neutropenia-related complications of chemotherapy. However, the administration of pegfilgrastim 24 hours after chemotherapy treatment often poses an inconvenience for the patients and care givers. Methods: A retrospective chart review of the patients who received pegfilgrastim the same day as chemotherapy was done at a rural oncology practice, where some patients travel more than 100 miles each way for treatment. Incidence of febrile neutropenia (FN), defined as ANC = 500 and temperature =101° F; hospitalization for FN; dose-delay; dose-reduction (= 15% reduction in the planned dose); ANC recovery (ANC=1,500 on the day of next treatment), Grade III/IV neutropenia one week after chemotherapy and empiric use of prophylactic antibiotics was recorded. Results: Between 11/05 and 11/06, 243 doses of pegfilgrastim were given the same day as chemotherapy to 48 patients. M/F=14/34; Age(median): 61.2 yrs (range: 27–88 yrs).The diagnoses were NSCLC=13 (Stage II, III, IV: 2, 2 and 9, respectively); Breast Ca=11 (stage II, III, IV: 4, 6 and 1, respectively); Diffuse large B-cell lymphoma (DLBCL)= 6 (stage III, IV: 4, 2); Follicular lymphoma=3 (stage IV: 3); SCLC=3 (stage IV: 3); Ovarian Ca=2 (stage III, IV: 1, 1); and others=10. Twenty five different chemotherapy regimens were administered (Q3 wk. regimens: 19, Q2 wk. regimens: 4 and others=2). No patient had FN or hospitalization for FN. ANC recovery was sub- optimal twice (ANC of 880 and 1,010 on the day of next treatment). Dose was delayed only once due to neutropenia (ANC=880), patient with ANC of 1,010 received treatment as scheduled. Three patients needed dose-reduction of more than 15% (all had DLBCL). Grade III/IV neutropenia after one week of treatment (CBC was available after 235 treatments) was observed in 52 (0.2%) patients, 23 patients had had AC and 25 patients had had CHOP. Empiric prophylactic antibiotics were not given to any patient. Conclusions: Pegfilgrastim can be effectively given the same day as chemotherapy to ameliorate neutropenia-related complications. Same-day administration of pegfilgrastim could improve the quality of life of patients and may reduce the health care costs. Large prospective trials should be done to address this issue.

No significant financial relationships to disclose.

Sensory hyperinnervation after neonatal skin wounding: effect of bupivacaine sciatic nerve block

The response to tissue injury includes sensitization of peripheral nociceptors and central neuronal pathways leading to acute clinical and inflammatory pain. A further response is sprouting of sensory nerve terminals in the region of skin damage. This hyperinnervation response is particularly intense in neonates compared with adults. In this study, we tested the effect of regional nerve block at the time of injury on skin hyperinnervation. Anaesthetized newborn rat pups were treated with percutaneous sciatic nerve block injections of 0.25% bupivacaine 25 microliters followed by a localized hindpaw skin wound. Cutaneous innervation was studied by image analysis of immunostained skin sections, 7 days after wounding, and sensory thresholds were assessed using von Frey hairs. The results showed that both hyperinnervation and hypersensitivity were not significantly altered by the application of a regional nerve block at the time of injury. This suggests that regional analgesia, used commonly in clinical practice, is unlikely to prevent the hyperinnervation that follows skin wounding.

Neonatally wounded skin induces NGF-independent sensory neurite outgrowth in vitro

An in vitro model was established to investigate factors underlying the sensory hyperinnervation of neonatal rat skin wounds that has been observed in vivo (Reynolds and Fitzgerald, J. Comp. Neurol. 358 (1995) 487-489). Explants of normal and wounded rat dorsal foot skin were co-cultured with explants of embryonic chick or newborn rat dorsal root ganglia for 24 h and the number of sensory neurites counted. Explants of skin surrounding a wound made at birth were taken 3 (P3) or 10 (P10) days later and compared with normal skin of the same age. In addition, explants were taken from adult skin wounded 3 and 10 days earlier. At P3, normal skin induced weak neurite outgrowth (mean 13.1 +/- 2.1 neurites per ganglion explant) but skin that had been wounded 3 days earlier, at birth, induced three times more neurite outgrowth (37.8 +/- 3.3). Ten days after wounding at birth, neurite outgrowth was still substantial (40.9 +/- 3.3) although at that age (P10), even normal skin stimulates substantial growth (37.4 +/- 2.9). Normal adult skin also stimulated neurite outgrowth (28.7 +/- 0.45) but this was not increased by wounding 3 or 10 days earlier, and this was enhanced 3 days but not 10 days after wounding. Anti-NGF (nerve growth factor) added to the culture medium blocked the constitutive neurite stimulating activity from normal P10 and adult skin but was ineffective in blocking the neurite stimulating activity produced by neonatal wounding. It is concluded that skin wounding at birth results in release of one or more sensory neurotrophic factors that stimulate rat and chick dorsal root ganglia neurite outgrowth for at least 10 days, but which do not include NGF.