Purpose: Many smaller studies have previously shown a significant association between thyroid autoantibody induced hypothyroidism and lower serum vitamin D levels. However, these finding have not been confirmed by large-scale studies. In this study, we evaluated the relationship between hypothyroidism and vitamin D levels using a large population-based data. Methods: For this study, we used National Health and Nutrition Examination Survey (NHANES) during the years 2007–2012. We categorized participants into three clinically relevant categories based on vitamin D levels: optimal, intermediate and deficient. Participants were also split into hypothyroid and hyperthyroid. Weighted multivariable logistic regression analyses were used to calculate the odds of being hypothyroid based on vitamin D status. Results: A total of 7943 participants were included in this study, of which 614 (7.7%) were having hypothyroidism. Nearly 25.6% of hypothyroid patients had vitamin D deficiency, compared to 20.6% among normal controls. Adjusted logistic regression analyses showed that the odds of developing hypothyroidism were significantly higher among patients with intermediate (adjusted odds ratio [aOR], 1.7, 95% CI: 1.5–1.8) and deficient levels of vitamin D (aOR, 1.6, 95% CI: 1.4–1.9). Conclusion: Low vitamin D levels are associated with autoimmune hypothyroidism. Healthcare initiatives such as mass vitamin D deficiency screening among at-risk population could significantly decrease the risk for hypothyroidism in the long-term.

Background: Metabolomic analysis aids in the identification of novel biomarkers by revealing the metabolic dysregulations underlying CVD etiology. The aim of this study was to evaluate which metabolic biomarkers could add value for the prognosis of CVD events using meta-analysis. Methods: The PRISMA guideline was followed for the systematic review. For the meta-analysis, biomarkers were included if they were tested in multivariate prediction models for fatal CVD outcomes. We grouped the metabolites in biological classes for subgroup analysis. We evaluated the prediction performance of models which reported discrimination and/or reclassification statistics. Results: For the systematic review, there were 22 studies which met the inclusion/exclusion criteria. For the meta-analysis, there were 41 metabolites grouped into 8 classes from 19 studies (45,420 subjects, 5,954 events). A total of 39 of the 41 metabolites were significant with a combined effect size of 1.14 (1.07-1.20). For the predictive performance assessment, there were 21 studies, 54,337 subjects 6,415 events. The average change in c-statistic after adding the biomarkers to a clinical model was 0.0417 (SE 0.008). Conclusion: This study provides evidence that metabolomic biomarkers, mainly lipid species, have the potential to provide additional prognostic value. Current data are promising, although approaches and results are heterogeneous.

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.

The purpose of this study was to examine trends in diabetes-related hospitalizations over the period 2010 to 2019 using Nationwide Inpatient Sample (NIS) to facilitate informed policies regarding diabetes-related prevention and management. Between 2010 and 2019, there were 304 million hospitalizations above 18 years of age, of which 78 million were diabetes-associated hospitalizations. The overall population-adjusted diabetes hospitalizations significantly increased from 3079.0 to 3280.8 per 100,000 US population (relative increase, 6.6%, Ptrend < 0.028). Age-stratified analysis showed that hospitalizations significantly increased for 18–29 years (relative increase, 7.8%, Ptrend < 0.001) while age- and gender-stratified analysis showed that diabetes hospitalization significantly increased for 18–29-year males (relative increase, 18.1%, Ptrend < 0.001). Total hospitalization charge increased from 97.5 billion USD in 2010 to 132.0 billion USD in 2019 (relative increase, 35.4%, Ptrend < 0.001). Our study’s findings suggest that diabetes-associated hospitalizations will continue to increase in the future because recent evidence indicates a reappearance of diabetes complications. It is important to screen, prevent, and control diabetes at a younger age based on the trends observed in our study.

The rates of both maternal and fetal adverse outcomes increase significantly with higher body mass index. The aim of this study was to calculate national estimates of adverse maternal and fetal outcomes and associated hospitalization cost among obese pregnant women using a national database. This study was a retrospective analysis of data retrieved from Nationwide Inpatient Sample database, collected during 2010–2014. The primary outcomes of this study were adverse maternal and fetal outcomes, hospital length of stay, and hospitalization cost. There was a total of 18,687,217 delivery-related hospitalizations, of which 1,048,323 were among obese women. Obese women were more likely to have cesarean deliveries (aOR 1.70, 95% CI 1.62–1.79) and labor inductions (aOR 1.51, 95% CI 1.42–1.60), greater length of stay after cesarean deliveries (aOR 1.14, 95% CI 1.08–1.36) and vaginal deliveries (aOR 1.48, 95% CI 1.23–1.77). They were also more likely to have pregnancy-related hypertension, preeclampsia, gestational diabetes, premature rupture of membranes, chorioamnionitis, venous thromboembolism, excessive fetal growth, and fetal distress. Obese pregnant women had significantly greater risk for adverse obstetrical outcomes, which substantially increased the hospital and economic burden. Risk stratification of pregnant patients based on obesity could also help obstetricians to make better clinical decisions and improve patient outcomes.

Phase I studies are used to estimate the dose-toxicity profile of the drugs and to select appropriate doses for successive studies. However, literature on statistical methods used for phase I studies are extensive. The objective of this review is to provide a concise summary of existing and emerging techniques for selecting dosages that are appropriate for phase I cancer trials. Many advanced statistical studies have proposed novel and robust methods for adaptive designs that have shown significant advantages over conventional dose finding methods. An increasing number of phase I cancer trials use adaptive designs, particularly during the early phases of the study. In this review, we described nonparametric and algorithm-based designs such as traditional 3 + 3, accelerated titration, Bayesian algorithm-based design, up-and-down design, and isotonic design. In addition, we also described parametric model-based designs such as continual reassessment method, escalation with overdose control, and Bayesian decision theoretic and optimal design. Ongoing studies have been continuously focusing on improving and refining the existing models as well as developing newer methods. This study would help readers to assimilate core concepts and compare different phase I statistical methods under one banner. Nevertheless, other evolving methods require future reviews.