Non-Classical Role of Vitamin D and Development of Optimal Vitamin D Cut-Offs for Cardiametabolic Health Outcomes

Over the last decade, vitamin D deficiency has emerged as a potential risk factor for the development of cardiometabolic diseases. However, the evidence from epidemiological studies and randomized controlled trials (RCT) has yielded conflicting results. Moreover, vitamin D guidelines by the Institute of Medicine and the Endocrine Society have led to substantial disagreement about what defines optimal levels of vitamin D status, owing in part to the inter-laboratory differences in the measurement of vitamin D status (as measured by total 25-hydroxyvitamin D [25(OH)D]) and the inconsistent findings from epidemiological and RCT data in relation to non-skeletal health outcomes. For non-skeletal health outcomes, disagreement still exists about whether the optimal level of 25(OH)D is higher than the currently recommended levels of 25(OH)D for bone health. Therefore, the objectives of this dissertation were; i) to assess the dose-response relationship between standardized total 25(OH)D levels and cardiometabolic health outcomes; ii) to develop optimal vitamin D cutoffs in relation to cardiometabolic health, and; iii) to assess the clinical utility of total 25(OH)D as a biomarker for adverse cardiometabolic health outcomes. Studies 1 and 2 used cross-sectional data from the National Health and Nutrition Examination Survey (NHANES, 2001-2010), and studies 3 and 4 used prospective data from NHANES III (1988-1994) mortality follow-up. Standardized total 25(OH)D data was used in all four studies. In study 1, results showed that a higher total 25(OH)D was inversely associated with cardiometabolic disease, irrespective of race/ethnicity. In study 2, the optimal total 25(OH)D associated with normal glucose and insulin homeostasis was estimated at 60 nmol/L overall, but differed by race/ethnicity (non-Hispanic whites: 68 nmol/L, non-Hispanic blacks: 41 nmol/L, and Mexican-Americans: 54 nmol/L). In study 3, low total 25(OH)D (<50 nmol/L) exacerbated the risk of cardiometabolic mortality associated with metabolic dysfunction in normal-weight and obesity groups. Finally, in study 4, a single measurement of total 25(OH)D <30 nmol/L in middle- to older-aged adults was associated with high lifetime risk of cardiometabolic mortality, particularly among those with 2 major traditional CVD risk factors. Taken together, these findings suggest that low total 25(OH)D is a strong risk marker of adverse cardiometabolic health outcomes

What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?

Background: Tumor size assessment by MRI is central for staging uterine cervical cancer. However, the optimal role of MRI-derived tumor measurements for prognostication is still unclear. Material and methods: This retrospective cohort study included 416 women (median age: 43 years) diagnosed with cervical cancer during 2002–2017 who underwent pretreatment pelvic MRI. The MRIs were independently read by three radiologists, measuring maximum tumor diameters in three orthogonal planes and maximum diameter irrespective of plane (MAXimaging). Inter-reader agreement for tumor size measurements was assessed by intraclass correlation coefficients (ICCs). Size was analyzed in relation to age, International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, histopathological markers, and disease-specific survival using Kaplan–Meier-, Cox regression-, and time-dependent receiver operating characteristics (tdROC) analyses. Results: All MRI tumor size variables (cm) yielded high areas under the tdROC curves (AUCs) for predicting survival (AUC 0.81–0.84) at 5 years after diagnosis and predicted outcome (hazard ratios [HRs] of 1.42–1.76, p < 0.001 for all). Only MAXimaging independently predicted survival (HR = 1.51, p = 0.03) in the model including all size variables. The optimal cutoff for maximum tumor diameter (≥ 4.0 cm) yielded sensitivity (specificity) of 83% (73%) for predicting disease-specific death after 5 years. Inter-reader agreement for MRI-based primary tumor size measurements was excellent, with ICCs of 0.83–0.85. Conclusion: Among all MRI-derived tumor size measurements, MAXimaging was the only independent predictor of survival. MAXimaging ≥ 4.0 cm represents the optimal cutoff for predicting long-term disease-specific survival in cervical cancer. Inter-reader agreement for MRI-based tumor size measurements was excellent.publishedVersio

Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

There is an emerging need in clinical research to accurately predict patients disease status and disease progression by optimally integrating multivariate clinical information. Clinical data is often collected over time for multiple biomarkers of different types (e.g. continuous, binary, counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a 5-year follow up period

Prognostic value of soluble ST2, high-sensitivity cardiac troponin, and NT-proBNP in type 2 diabetes: a 15-year retrospective study

Background: Patients with type 2 diabetes (T2DM) present an increased risk of cardiovascular (CV) disease and excess CV-related mortality. Beyond the established role of brain natriuretic peptide (BNP) and cardiac troponins (cTn), other non-cardiac-specific biomarkers are emerging as predictors of CV outcomes in T2DM. Methods: Serum levels of soluble suppression of tumorigenesis 2 (sST2), high-sensitivity (hs)-cTnI, and N-terminal (NT)-proBNP were assessed in 568 patients with T2DM and 115 healthy controls (CTR). Their association with all-cause mortality and the development of diabetic complications was tested in T2DM patients over a median follow-up of 16.8 years using Cox models and logistic regressions. Results: sST2 followed an increasing trend from CTR to uncomplicated T2DM patients (T2DM-NC) to patients with at least one complication (T2DM-C), while hs-cTnI was significantly higher in T2DM-C compared to CTR but not to T2DM-NC. A graded association was found between sST2 (HR 2.76 [95% CI 1.20-6.33] for ≥ 32.0 ng/mL and 2.00 [1.02-3.94] for 16.5-32.0 ng/mL compared to &lt; 16.5 ng/mL, C-statistic = 0.729), NT-proBNP (HR 2.04 [1.90-4.55] for ≥ 337 ng/L and 1.48 [1.05-2.10] for 89-337 ng/L compared to &lt; 89 ng/L, C-statistic = 0.741), and 15-year mortality in T2DM, whereas increased mortality was observed in patients with hs-cTnI ≥ 7.8 ng/L (HR 1.63 [1.01-2.62]). A 'cardiac score' based on the combination of sST2, hs-cTnI, and NT-proBNP was significantly associated with all-cause mortality (HR 1.35 [1.19-1.53], C-statistic = 0.739) and development of CV events. Conclusions: sST2, hs-cTnI, and NT-proBNP are associated with 15-year mortality and onset of CV events in T2DM. The long-term prognostic value of sST2 and its ability to track variables related to insulin resistance and associated metabolic disorders support its implementation into routine clinical practice

How well do neurosurgeons predict survival in patients with high-grade glioma?

Due to the lack of reliable prognostic tools, prognostication and surgical decisions largely rely on the neurosurgeons’ clinical prediction skills. The aim of this study was to assess the accuracy of neurosurgeons’ prediction of survival in patients with high-grade glioma and explore factors possibly associated with accurate predictions. In a prospective single-center study, 199 patients who underwent surgery for high-grade glioma were included. After surgery, the operating surgeon predicted the patient’s survival using an ordinal prediction scale. A survival curve was used to visualize actual survival in groups based on this scale, and the accuracy of clinical prediction was assessed by comparing predicted and actual survival. To investigate factors possibly associated with accurate estimation, a binary logistic regression analysis was performed. The surgeons were able to diferentiate between patients with diferent lengths of survival, and median survival fell within the predicted range in all groups with predicted survival24 months, median survival was shorter than predicted. The overall accuracy of surgeons’ survival estimates was 41%, and over- and underestimations were done in 34% and 26%, respectively. Consultants were 3.4 times more likely to accurately predict survival compared to residents (p=0.006). Our fndings demonstrate that although especially experienced neurosurgeons have rather good predictive abilities when estimating survival in patients with high-grade glioma on the group level, they often miss on the individual level. Future prognostic tools should aim to beat the presented clinical prediction skills.publishedVersio

Using population-based data to evaluate the impact of adherence to endocrine therapy on survival in breast cancer through the web-application BreCanSurvPred

We show how the use and interpretation of population-based cancer survival indicators can help oncologists talk with breast cancer (BC) patients about the relationship between their prognosis and their adherence to endocrine therapy (ET). The study population comprised a population-based cohort of estrogen receptor positive BC patients (N = 1268) diagnosed in Girona and Tarragona (Northeastern Spain) and classified according to HER2 status (+ / -), stage at diagnosis (I/II/III) and five-year cumulative adherence rate (adherent > 80%; non-adherent <= 80%). Cox regression analysis was performed to identify significant prognostic factors for overall survival, whereas relative survival (RS) was used to estimate the crude probability of death due to BC (PBC). Stage and adherence to ET were the significant factors for predicting all-cause mortality. Compared to stage I, risk of death increased in stage II (hazard ratio [HR] 2.24, 95% confidence interval [CI]: 1.51-3.30) and stage III (HR 5.11, 95% CI 3.46-7.51), and it decreased with adherence to ET (HR 0.57, 95% CI 0.41-0.59). PBC differences were higher in non-adherent patients compared to adherent ones and increased across stages: stage I: 6.61% (95% CI 0.05-13.20); stage II: 9.77% (95% CI 0.59-19.01), and stage III: 22.31% (95% CI 6.34-38.45). The age-adjusted survival curves derived from this modeling were implemented in the web application BreCanSurvPred (https://pdocomputation.snpstats.net/BreCanSurvPred). Web applications like BreCanSurvPred can help oncologists discuss the consequences of non-adherence to prescribed ET with patients