Publications

Background: Dietary modification remains an essential component of successful weight loss strategies. No one dietary strategy has been determined to be superior to others for the general population. Studies that contrast reducing dietary fat vs. carbohydrate report consistently high within-group variability in dietary adherence and weight loss. Previous research by our group and others suggest that insulin-glucose dynamics or genotype patterns may modify diet effects. Objective: To determine if within-group weight loss variability on a Healthy Low-Fat (HLF) vs. a Healthy Low Carbohydrate (HLC) diet can be attributed to underlying factors such as insulin-glucose dynamics (i.e., insulin resistance and secretion) or genotype pattern. We hypothesized the above factors would be effect modifiers of HLF and HLC diets on 12-month weight loss. Methods: Generally healthy, non-diabetic adults, 18-50 years, BMI 28-40 kg/m 2 , were randomized to HLF or HLC with no specific prescribed energy restriction for 12 months (n=609). Health educators delivered the intervention in 22 1-hr group classes. Data were collected at 0, 3, 6, & 12 months. Dietary intake was assessed by three 24-hour recalls/time point. Clinical data includes: 75-g glucose oral glucose tolerance tests (insulin concentration at 30 minutes [Ins-30], a measure of insulin secretion), genotyping (3-SNP multilocus genotype: Low-Fat Genotype vs. Low-Carb Genotype, UK Biobank Axiom® array), body composition (DXA), resting energy expenditure (indirect calorimetry), epigenetics, proteomics, subcutaneous adipose tissue, microbiota, and standard CVD risk indicators. Results: At 12 months participants collectively lost 6,559 lbs. Retention was 79%, with equal dropout between arms. Range of weight change in both diet arms was ~80 lbs (-60 to +20 lbs). Macronutrient distribution at 12 months was 48% vs. 30% carbohydrate, 29% vs. 45% fat, and 21% vs. 23% protein for HLF and HLC, respectively. Both groups reported achieving and maintaining an average ~500 kcal deficit relative to baseline. Weight loss was similar for HLF vs. HLC: -12.1 ± 1.1 lbs vs. -13.8 ± 1.0 lbs, mean ± SEM. Neither Ins-30 (p for interaction = 0.84) nor genotype pattern (p for interaction = 0.20) modified the effect of diet on 12-month weight loss. Conclusions: Despite substantial weight loss, high within-group variability, and strong dietary differentiation between groups, neither baseline Ins-30 nor genotype pattern modified the effect of diet on 12-month weight loss. Focus on a healthy diet in both diet arms is novel in the context of many previous Low-Fat vs. Low-Carb studies and may have diminished expected effect modification. The extensive data set collected will be used to explore this and other potential explanatory factors.

A flexible communication mechanism is a desirable feature in multiprocessors because it allows support for multiple communication protocols, expands performance monitoring capabilities, and leads to a simpler design and debug process. In the Stanford FLASH multiprocessor, flexibility is obtained by requiring all transactions in a node to pass through a programmable node controller, called MAGIC. In this paper, we evaluate the performance costs of flexibility by comparing the performance of FLASH to that of an idealized hardwired machine on representative parallel applications and a multiprogramming workload. To measure the performance of FLASH, we use a detailed simulator of the FLASH and MAGIC designs, together with the code sequences that implement the cache-coherence protocol. We find that for a range of optimized parallel applications the performance differences between the idealized machine and FLASH are small. For these programs, either the miss rates are small or the latency of the programmable protocol can be hidden behind the memory access time. For applications that incur a large number of remote misses or exhibit substantial hot-spotting, performance is poor for both machines, though the increased remote access latencies or the occupancy of MAGIC lead to lower performance for the flexible design. In most cases, however, FLASH is only 2%–12% slower than the idealized machine.

Serious concerns about research quality have catalysed a number of reform initiatives intended to improve transparency and reproducibility and thus facilitate self-correction, increase efficiency and enhance research credibility. Meta-research has evaluated the merits of some individual initiatives; however, this may not capture broader trends reflecting the cumulative contribution of these efforts. In this study, we manually examined a random sample of 250 articles in order to estimate the prevalence of a range of transparency and reproducibility-related indicators in the social sciences literature published between 2014 and 2017. Few articles indicated availability of materials (16/151, 11% [95% confidence interval, 7% to 16%]), protocols (0/156, 0% [0% to 1%]), raw data (11/156, 7% [2% to 13%]) or analysis scripts (2/156, 1% [0% to 3%]), and no studies were pre-registered (0/156, 0% [0% to 1%]). Some articles explicitly disclosed funding sources (or lack of; 74/236, 31% [25% to 37%]) and some declared no conflicts of interest (36/236, 15% [11% to 20%]). Replication studies were rare (2/156, 1% [0% to 3%]). Few studies were included in evidence synthesis via systematic review (17/151, 11% [7% to 16%]) or meta-analysis (2/151, 1% [0% to 3%]). Less than half the articles were publicly available (101/250, 40% [34% to 47%]). Minimal adoption of transparency and reproducibility-related research practices could be undermining the credibility and efficiency of social science research. The present study establishes a baseline that can be revisited in the future to assess progress.

The coronavirus disease 2019 (COVID-19) pandemic incited a global clinical trial research agenda of unprecedented speed and high volume. This expedited research activity in a time of crisis produced both successes and failures that offer valuable learning opportunities for the scientific community to consider. Successes include the implementation of large adaptive and pragmatic trials as well as burgeoning efforts toward rapid data synthesis and open science principles. Conversely, notable failures include: (1) inadequate study design and execution; (2) data reversal, fraud, and retraction; and (3) research duplication and waste. Other challenges that became highlighted were the need to find unbiased designs for investigating complex, nonpharmaceutical interventions and the use of routinely collected data for outcomes assessment. This article discusses these issues juxtaposing the COVID-19 trials experience against trials in anesthesiology and other fields. These lessons may serve as a positive catalyst for transforming future clinical trial research.

Identifying modifiable factors through environmental research may improve mental health outcomes. However, several challenges need to be addressed to optimize the chances of success. By analyzing the Netherlands Mental Health Survey and Incidence Study-2 data, we provide a data-driven illustration of how closely connected the exposures and the mental health outcomes are and how model and variable specifications produce "vibration of effects" (variation of results under multiple different model specifications). Interdependence of exposures is the rule rather than the exception. Therefore, exposure-wide systematic approaches are needed to separate genuine strong signals from selective reporting and dissect sources of heterogeneity. Pre-registration of protocols and analytical plans is still uncommon in environmental research. Different studies often present very different models, including different variables, despite examining the same outcome, even if consistent sets of variables and definitions are available. For datasets that are already collected (and often already analyzed), the exploratory nature of the work should be disclosed. Exploratory analysis should be separated from prospective confirmatory research with truly pre-specified analysis plans. In the era of big-data, where very low P values for trivial effects are detected, several safeguards may be considered to improve inferences, eg, lowering P-value thresholds, prioritizing effect sizes over significance, analyzing pre-specified falsification endpoints, and embracing alternative approaches like false discovery rates and Bayesian methods. Any claims for causality should be cautious and preferably avoided, until intervention effects have been validated. We hope the propositions for amendments presented here may help with meeting these pressing challenges.

microRNAs (miRNAs) are fundamental to cellular biology. Although only approximately 22 bases long, miRNAs regulate complex processes in health and disease, including human cancer. Because miRNAs are highly stable in circulation when compared with several other classes of nucleic acids, they have generated intense interest as clinical biomarkers in diverse epidemiologic studies. As with other molecular biomarker fields, however, miRNA research has become beleaguered by pitfalls related to terminology and classification; procedural, assay, and study cohort heterogeneity; and methodological inconsistencies. Together, these issues have led to both false-positive and potentially false-negative miRNA associations. In this review, we summarize the biological rationale for studying miRNAs in human disease with a specific focus on circulating miRNAs, which highlight some of the most challenging topics in the field to date. Examples from lung cancer are used to illustrate the potential utility and some of the pitfalls in contemporary miRNA research. Although the field is in its infancy, several important lessons have been learned relating to cohort development, sample preparation, and statistical analysis that should be considered for future studies. The goal of this primer is to equip epidemiologists and clinical researchers with sound principles of study design and analysis when using miRNAs.