Critical updates in neuroendocrine tumors: Version 9 American Joint Committee on Cancer staging system for gastroenteropancreatic neuroendocrine tumors.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including gastroenteropancreatic neuroendocrine tumors (GEP-NETs), is meant to be dynamic, requiring periodic updates to optimize AJCC staging definitions. This entails the collaboration of experts charged with evaluating new evidence that supports changes to each staging system. GEP-NETs are the second most prevalent neoplasm of gastrointestinal origin after colorectal cancer. Since publication of the AJCC eighth edition, the World Health Organization has updated the classification and separates grade 3 GEP-NETs from poorly differentiated neuroendocrine carcinoma. In addition, because of major advancements in diagnostic and therapeutic technologies for GEP-NETs, AJCC version 9 advocates against the use of serum chromogranin A for the diagnosis and monitoring of GEP-NETs. Furthermore, AJCC version 9 recognizes the increasing role of endoscopy and endoscopic resection in the diagnosis and management of NETs, particularly in the stomach, duodenum, and colorectum. Finally, T1NXM0 has been added to stage I in these disease sites as well as in the appendix.

Delineation of two multi-invasion-induced rearrangement pathways that differently affect genome stability.

Punctuated bursts of structural genomic variations (SVs) have been described in various organisms, but their etiology remains incompletely understood. Homologous recombination (HR) is a template-guided mechanism of repair of DNA double-strand breaks and stalled or collapsed replication forks. We recently identified a DNA break amplification and genome rearrangement pathway originating from the endonucleolytic processing of a multi-invasion (MI) DNA joint molecule formed during HR. Genome-wide approaches confirmed that multi-invasion-induced rearrangement (MIR) frequently leads to several repeat-mediated SVs and aneuploidies. Using molecular and genetic analysis and a novel, highly sensitive proximity ligation-based assay for chromosomal rearrangement quantification, we further delineate two MIR subpathways. MIR1 is a universal pathway occurring in any sequence context, which generates secondary breaks and frequently leads to additional SVs. MIR2 occurs only if recombining donors exhibit substantial homology and results in sequence insertion without additional breaks or SVs. The most detrimental MIR1 pathway occurs late on a subset of persisting DNA joint molecules in a PCNA/Polδ-independent manner, unlike recombinational DNA synthesis. This work provides a refined mechanistic understanding of these HR-based SV formation pathways and shows that complex repeat-mediated SVs can occur without displacement DNA synthesis. Sequence signatures for inferring MIR1 from long-read data are proposed.

Survival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including anal cancer, is the standard for cancer staging in the United States. The AJCC staging criteria are dynamic, and periodic updates are conducted to optimize AJCC staging definitions through a panel of experts charged with evaluating new evidence to implement changes. With greater availability of large data sets, the AJCC has since restructured and updated its processes, incorporating prospectively collected data to validate stage group revisions in the version 9 AJCC staging system, including anal cancer. Survival analysis using AJCC eighth edition staging guidelines revealed a lack of hierarchical order in which stage IIIA anal cancer was associated with a better prognosis than stage IIB disease, suggesting that, for anal cancer, tumor (T) category has a greater effect on survival than lymph node (N) category. Accordingly, version 9 stage groups have been appropriately adjusted to reflect contemporary long-term outcomes. This article highlights the changes to the now published AJCC staging system for anal cancer, which: (1) redefined stage IIB as T1-T2N1M0 disease, (2) redefined stage IIIA as T3N0-N1M0 disease, and (3) eliminated stage 0 disease from its guidelines altogether.

Survival outcomes used to validate version 9 of the American Joint Committee on Cancer staging system for appendiceal cancer.

The standard for cancer staging in the United States for all cancer sites, including primary carcinomas of the appendix, is the American Joint Committee on Cancer (AJCC) staging system. AJCC staging criteria undergo periodic revisions, led by a panel of site-specific experts, to maintain contemporary staging definitions through the evaluation of new evidence. Since its last revision, the AJCC has restructured its processes to include prospectively collected data because large data sets have become increasingly robust and available over time. Thus survival analyses using AJCC eighth edition staging criteria were used to inform stage group revisions in the version 9 AJCC staging system, including appendiceal cancer. Although the current AJCC staging definitions were maintained for appendiceal cancer, incorporating survival analysis into the version 9 staging system provided unique insight into the clinical challenges in staging rare malignancies. This article highlights the critical clinical components of the now published version 9 AJCC staging system for appendix cancer, which (1) justified the separation of three different histologies (non-mucinous, mucinous, signet-ring cell) in terms of prognostic variance, (2) demonstrated the clinical implications and challenges in staging heterogeneous and rare tumors, and (3) emphasized the influence of data limitations on survival analysis for low-grade appendiceal mucinous neoplasms.

The new (Version 9) American Joint Committee on Cancer tumor, node, metastasis staging for cervical cancer.

The American Joint Committee on Cancer (AJCC) tumor, node, metastasis (TNM) staging for all cancer sites has been periodically updated as a published manual for many years. The last update, the eighth edition AJCC Cancer Staging Manual went into use on January 1, 2018. The AJCC has since restructured and updated its processes, and all AJCC staging-related data are now housed on its new application programming interface. Consequently, the next AJCC TNM staging update, AJCC version 9 TNM staging, will be published electronically and will be released chapter by chapter. The first chapter of version 9 AJCC TNM staging is the updated cervical cancer staging, which is now published. This article highlights the changes to the AJCC TNM cervical cancer staging; these changes align with the International Federation of Gynecology and Obstetrics staging. The most important of the changes are: 1) the incorporation of imaging and surgical findings, 2) the elimination of lateral spread from T1a, 3) the addition of a subcategory to T1b (T1b3), and 4) histopathology is updated to reflect human papillomavirus-associated and human papillomavirus-independent carcinomas.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Intergenomic signatures of coevolution between Tasmanian devils and an infectious cancer.

Coevolution is common and frequently governs host-pathogen interaction outcomes. Phenotypes underlying these interactions often manifest as the combined products of the genomes of interacting species, yet traditional quantitative trait mapping approaches ignore these intergenomic interactions. Devil facial tumor disease (DFTD), an infectious cancer afflicting Tasmanian devils (Sarcophilus harrisii), has decimated devil populations due to universal host susceptibility and a fatality rate approaching 100%. Here, we used a recently developed joint genome-wide association study (i.e., co-GWAS) approach, 15 y of mark-recapture data, and 960 genomes to identify intergenomic signatures of coevolution between devils and DFTD. Using a traditional GWA approach, we found that both devil and DFTD genomes explained a substantial proportion of variance in how quickly susceptible devils became infected, although genomic architectures differed across devils and DFTD; the devil genome had fewer loci of large effect whereas the DFTD genome had a more polygenic architecture. Using a co-GWA approach, devil-DFTD intergenomic interactions explained ~3× more variation in how quickly susceptible devils became infected than either genome alone, and the top genotype-by-genotype interactions were significantly enriched for cancer genes and signatures of selection. A devil regulatory mutation was associated with differential expression of a candidate cancer gene and showed putative allele matching effects with two DFTD coding sequence variants. Our results highlight the need to account for intergenomic interactions when investigating host-pathogen (co)evolution and emphasize the importance of such interactions when considering devil management strategies.

Dual-axial engineering on atomically dispersed catalysts for ultrastable oxygen reduction in acidic and alkaline solutions.

Atomically dispersed catalysts are a promising alternative to platinum group metal catalysts for catalyzing the oxygen reduction reaction (ORR), while limited durability during the electrocatalytic process severely restricts their practical application. Here, we report an atomically dispersed Co-doped carbon-nitrogen bilayer catalyst with unique dual-axial Co-C bonds (denoted as Co/DACN) by a smart phenyl-carbon-induced strategy, realizing highly efficient electrocatalytic ORR in both alkaline and acidic media. The corresponding half-wave potential for ORR is up to 0.85 and 0.77 V (vs. reversible hydrogen electrode (RHE)) in 0.5 M H2SO4 and 0.1 M KOH, respectively, representing the best ORR activity among all non-noble metal catalysts reported to date. Impressively, the Zn-air battery (ZAB) equipped with Co/DACN cathode achieves outstanding durability after 1,688 h operation at 10 mA cm-2 with a high current density (154.2 mA cm-2) and a peak power density (210.1 mW cm-2). Density functional theory calculations reveal that the unique dual-axial cross-linking Co-C bonds of Co/DACN significantly enhance the stability during ORR and also facilitate the 4e- ORR pathway by forming a joint electron pool due to the improved interlayer electron mobility. We believe that axial engineering opens a broad avenue to develop high-performance heterogeneous electrocatalysts for advanced energy conversion and storage.

Deletion of Trps1 regulatory elements recapitulates postnatal hip joint abnormalities and growth retardation of Trichorhinophalangeal syndrome in mice.

Trichorhinophalangeal syndrome (TRPS) is a genetic disorder caused by point mutations or deletions in the gene-encoding transcription factor TRPS1. TRPS patients display a range of skeletal dysplasias, including reduced jaw size, short stature, and a cone-shaped digit epiphysis. Certain TRPS patients experience early onset coxarthrosis that leads to a devastating drop in their daily activities. The etiologies of congenital skeletal abnormalities of TRPS were revealed through the analysis of Trps1 mutant mouse strains. However, early postnatal lethality in Trps1 knockout mice has hampered the study of postnatal TRPS pathology. Here, through epigenomic analysis we identified two previously uncharacterized candidate gene regulatory regions in the first intron of Trps1. We deleted these regions, either individually or simultaneously, and examined their effects on skeletal morphogenesis. Animals that were deleted individually for either region displayed only modest phenotypes. In contrast, the Trps1Δint/Δint mouse strain with simultaneous deletion of both genomic regions exhibit postnatal growth retardation. This strain displayed delayed secondary ossification center formation in the long bones and misshaped hip joint development that resulted in acetabular dysplasia. Reducing one allele of the Trps1 gene in Trps1Δint mice resulted in medial patellar dislocation that has been observed in some patients with TRPS. Our novel Trps1 hypomorphic strain recapitulates many postnatal pathologies observed in human TRPS patients, thus positioning this strain as a useful animal model to study postnatal TRPS pathogenesis. Our observations also suggest that Trps1 gene expression is regulated through several regulatory elements, thus guaranteeing robust expression maintenance in skeletal cells.

Joint models reveal genetic architecture of pubertal stage transitions and their association with BMI in admixed Chilean population.

Early or late pubertal onset can lead to disease in adulthood, including cancer, obesity, type 2 diabetes, metabolic disorders, bone fractures, and psychopathologies. Thus, knowing the age at which puberty is attained is crucial as it can serve as a risk factor for future diseases. Pubertal development is divided into five stages of sexual maturation in boys and girls according to the standardized Tanner scale. We performed genome-wide association studies (GWAS) on the "Growth and Obesity Chilean Cohort Study" cohort composed of admixed children with mainly European and Native American ancestry. Using joint models that integrate time-to-event data with longitudinal trajectories of body mass index (BMI), we identified genetic variants associated with phenotypic transitions between pairs of Tanner stages. We identified $42$ novel significant associations, most of them in boys. The GWAS on Tanner $3\rightarrow 4$ transition in boys captured an association peak around the growth-related genes LARS2 and LIMD1 genes, the former of which causes ovarian dysfunction when mutated. The associated variants are expression and splicing Quantitative Trait Loci regulating gene expression and alternative splicing in multiple tissues. Further, higher individual Native American genetic ancestry proportions predicted a significantly earlier puberty onset in boys but not in girls. Finally, the joint models identified a longitudinal BMI parameter significantly associated with several Tanner stages' transitions, confirming the association of BMI with pubertal timing.

scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.

MOTIVATION: The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements. AVAILABILITY AND IMPLEMENTATION: scMNMF code can be found at https://github.com/yushanqiu/scMNMF.

Proactive or reactive? Neural oscillatory insight into the leader-follower dynamics of early infant-caregiver interaction.

We know that infants' ability to coordinate attention with others toward the end of the first year is fundamental to language acquisition and social cognition. Yet, we understand little about the neural and cognitive mechanisms driving infant attention in shared interaction: do infants play a proactive role in creating episodes of joint attention? Recording electroencephalography (EEG) from 12-mo-old infants while they engaged in table-top play with their caregiver, we examined the communicative behaviors and neural activity preceding and following infant- vs. adult-led joint attention. Infant-led episodes of joint attention appeared largely reactive: they were not associated with increased theta power, a neural marker of endogenously driven attention, and infants did not increase their ostensive signals before the initiation. Infants were, however, sensitive to whether their initiations were responded to. When caregivers joined their attentional focus, infants showed increased alpha suppression, a pattern of neural activity associated with predictive processing. Our results suggest that at 10 to 12 mo, infants are not routinely proactive in creating joint attention episodes yet. They do, however, anticipate behavioral contingency, a potentially foundational mechanism for the emergence of intentional communication.

Bimodal buckling governs human fingers' luxation.

Equilibrium bifurcation in natural systems can sometimes be explained as a route to stress shielding for preventing failure. Although compressive buckling has been known for a long time, its less-intuitive tensile counterpart was only recently discovered and yet never identified in living structures or organisms. Through the analysis of an unprecedented all-in-one paradigm of elastic instability, it is theoretically and experimentally shown that coexistence of two curvatures in human finger joints is the result of an optimal design by nature that exploits both compressive and tensile buckling for inducing luxation in case of traumas, so realizing a unique mechanism for protecting tissues and preventing more severe damage under extreme loads. Our findings might pave the way to conceive complex architectured and bio-inspired materials, as well as next generation artificial joint prostheses and robotic arms for bio-engineering and healthcare applications.

Large-scale climate patterns offer preseasonal hints on the co-occurrence of heat wave and O3 pollution in China.

Heat waves and air pollution extremes exert compounding effects on human health and food security and may worsen under future climate change. On the basis of reconstructed daily O3 levels in China and meteorological reanalysis, we found that the interannual variability of the frequency of summertime co-occurrence of heat wave and O3 pollution in China is regulated mainly by a combination of springtime warming in the western Pacific Ocean, western Indian Ocean, and Ross Sea. These sea surface temperature anomalies impose influences on precipitation, radiation, etc., to modulate the co-occurrence, which were also confirmed with coupled chemistry-climate numerical experiments. We thus built a multivariable regression model to predict co-occurrence a season in advance, and correlation coefficient could reach 0.81 (P < 0.01) for the North China Plain. Our results provide useful information for the government to take actions in advance to mitigate damage from these synergistic costressors.

Practical Guidance for Clinical Microbiology Laboratories: Microbiologic diagnosis of implant-associated infections.

SUMMARYImplant-associated infections (IAIs) pose serious threats to patients and can be associated with significant morbidity and mortality. These infections may be difficult to diagnose due, in part, to biofilm formation on device surfaces, and because even when microbes are found, their clinical significance may be unclear. Despite recent advances in laboratory testing, IAIs remain a diagnostic challenge. From a therapeutic standpoint, many IAIs currently require device removal and prolonged courses of antimicrobial therapy to effect a cure. Therefore, making an accurate diagnosis, defining both the presence of infection and the involved microorganisms, is paramount. The sensitivity of standard microbial culture for IAI diagnosis varies depending on the type of IAI, the specimen analyzed, and the culture technique(s) used. Although IAI-specific culture-based diagnostics have been described, the challenge of culture-negative IAIs remains. Given this, molecular assays, including both nucleic acid amplification tests and next-generation sequencing-based assays, have been used. In this review, an overview of these challenging infections is presented, as well as an approach to their diagnosis from a microbiologic perspective.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

GDF5+ chondroprogenitors derived from human pluripotent stem cells preferentially form permanent chondrocytes.

It has been established in the mouse model that during embryogenesis joint cartilage is generated from a specialized progenitor cell type, distinct from that responsible for the formation of growth plate cartilage. We recently found that mesodermal progeny of human pluripotent stem cells gave rise to two types of chondrogenic mesenchymal cells in culture: SOX9+ and GDF5+ cells. The fast-growing SOX9+ cells formed in vitro cartilage that expressed chondrocyte hypertrophy markers and readily underwent mineralization after ectopic transplantation. In contrast, the slowly growing GDF5+ cells derived from SOX9+ cells formed cartilage that tended to express low to undetectable levels of chondrocyte hypertrophy markers, but expressed PRG4, a marker of embryonic articular chondrocytes. The GDF5+-derived cartilage remained largely unmineralized in vivo. Interestingly, chondrocytes derived from the GDF5+ cells seemed to elicit these activities via non-cell-autonomous mechanisms. Genome-wide transcriptomic analyses suggested that GDF5+ cells might contain a teno/ligamento-genic potential, whereas SOX9+ cells resembled neural crest-like progeny-derived chondroprogenitors. Thus, human pluripotent stem cell-derived GDF5+ cells specified to generate permanent-like cartilage seem to emerge coincidentally with the commitment of the SOX9+ progeny to the tendon/ligament lineage.

Hyaline cartilage differentiation of fibroblasts in regeneration and regenerative medicine.

Amputation injuries in mammals are typically non-regenerative; however, joint regeneration is stimulated by BMP9 treatment, indicating the presence of latent articular chondrocyte progenitor cells. BMP9 induces a battery of chondrogenic genes in vivo, and a similar response is observed in cultures of amputation wound cells. Extended cultures of BMP9-treated cells results in differentiation of hyaline cartilage, and single cell RNAseq analysis identified wound fibroblasts as BMP9 responsive. This culture model was used to identify a BMP9-responsive adult fibroblast cell line and a culture strategy was developed to engineer hyaline cartilage for engraftment into an acutely damaged joint. Transplanted hyaline cartilage survived engraftment and maintained a hyaline cartilage phenotype, but did not form mature articular cartilage. In addition, individual hypertrophic chondrocytes were identified in some samples, indicating that the acute joint injury site can promote osteogenic progression of engrafted hyaline cartilage. The findings identify fibroblasts as a cell source for engineering articular cartilage and establish a novel experimental strategy that bridges the gap between regeneration biology and regenerative medicine.

Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.

The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating the defined variability as well as the current level of the underlying longitudinal trajectory as covariates, which, together with the longitudinal model, constitutes the joint modeling framework in this article. Asymptotic properties of maximum likelihood estimators are established for the present joint model. Estimation is implemented via an Expectation-Maximization (EM) algorithm with fully exponential Laplace approximation used in E-step to reduce the computation burden due to the increase of the random effects dimension. Simulation studies are conducted to reveal the advantage of the proposed method over the two-stage method, as well as a simpler joint modeling approach which does not take into account biomarker variability. Finally, we apply our model to investigate the effect of systolic blood pressure variability on cardiovascular events in the Medical Research Council elderly trial, which is also the motivating example for this article.