Introduction Circulating immune markers may be associated with preeclampsia but further investigations in early pregnancy and among preeclampsia subtypes are warranted. We examined immune markers in 208 preeclamptic women and 411 normotensive controls. Methods Our study was nested within the Collaborative Perinatal Project. A total of 242 women had first trimester serum samples and 392 had second trimester serum samples. Preeclampsia was defined as hypertension >20 weeks of gestation with proteinuria or pulmonary edema, oliguria, or convulsions. Preterm preeclampsia was defined as preeclampsia with delivery less than 37 weeks of gestation. Associations between immune markers RANTES, interleukin (IL)-6, IL4, IL5, IL12, IL10, IL8, IL1-beta, interferon (IFN)-gamma, tumor necrosis factor (TNF)-alpha and beta, transforming growth factor (TGF)-beta and preeclampsia were explored using a modified version of cox regression developed to address data with non-detectable levels. Models were adjusted for body mass index, gestational age of blood sampling, fetal sex, smoking, socioeconomic status and maternal age. Results In first trimester samples, IL-12 was associated with preeclampsia (p=0.0255). IFN-gamma (p=0.0063), IL1-beta (p=0.0006), IL5 (p=0.0422) and TNFr (p=0.0460) were associated with preterm preeclampsia only. In second trimester samples, IL1-beta was associated with preeclampsia (p=0.0180) and term preeclampsia (p=0.0454). After correction for multiple comparisons, only IL1-beta remained associated with preterm preeclampsia in the first trimester (p=0.0288). Discussion Elevated first trimester IL1-beta appears to be associated with preterm preeclampsia. However, few associations were observed in the second trimester. Systemic immune markers alone may not be useful for preeclampsia prediction.

The majority of colon tumors are driven by aberrant Wnt signaling in intestinal stem cells, which mediates an efficient route toward initiating intestinal cancer. Natural lipophilic polyphenols and long-chain polyunsaturated fatty acids (PUFAs) generally suppress Wnt- and NF-κB- (nuclear factor-κ light-chain enhancer of activated B-cell) related pathways. However, the effects of these extrinsic agents on colonic leucine-rich repeat-containing G-protein-coupled receptor 5-positive (Lgr5+) stem cells, the cells of origin of colon cancer, have not been documented to date. Therefore, we examined the effect of n-3 PUFA and polyphenol (curcumin) combination on Lgr5+ stem cells during tumor initiation and progression in the colon compared with an n-6 PUFA-enriched control diet. Lgr5-EGFP-IRES-creERT2 knock-in mice were fed diets containing n-6 PUFA (control), n-3 PUFA, n-6 PUFA+curcumin or n-3 PUFA+curcumin for 3 weeks, followed by 6 azoxymethane (AOM) injections, and terminated 17 weeks after the last injection. To further elucidate the effects of the dietary bioactives at the tumor initiation stage, Lgr5+ stem cells were also assessed at 12 and 24 h post AOM injection. Only n-3 PUFA+curcumin feeding reduced nuclear β-catenin in aberrant crypt foci (by threefold) compared with control at the progression time point. n-3 PUFA+curcumin synergistically increased targeted apoptosis in DNA-damaged Lgr5+ stem cells by 4.5-fold compared with control at 12 h and maximally reduced damaged Lgr5+ stem cells at 24 h, down to the level observed in saline-treated mice. Finally, RNAseq analysis indicated that p53 signaling in Lgr5+ stem cells from mice exposed to AOM was uniquely upregulated only following n-3 PUFA+curcumin cotreatment. These novel findings demonstrate that Lgr5+ stem cells are uniquely responsive to external dietary cues following the induction of DNA damage, providing a therapeutic strategy for eliminating damaged Lgr5+ stem cells to reduce colon cancer initiation.

Background Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution). Objectives To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research. Methods We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM. Results Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect. Conclusions The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.

We develop a Bayes factor based testing procedure for comparing two population means in high dimensional settings. In ‘large-p-small-n’ settings, Bayes factors based on proper priors require eliciting a large and complex p×p covariance matrix, whereas Bayes factors based on Jeffrey’s prior suffer the same impediment as the classical Hotelling T2 test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that the Bayes factor be based on lower dimensional random projections of the high dimensional data vectors. We choose the prior under the alternative to maximize the power of the test for a fixed threshold level, yielding a restricted most powerful Bayesian test (RMPBT). The final test statistic is based on the ensemble of Bayes factors corresponding to multiple replications of randomly projected data. We show that the test is unbiased and, under mild conditions, is also locally consistent. We demonstrate the efficacy of the approach through simulated and real data examples.