William S Yancy’s research while affiliated with Microbiome Core Facility USA and other places

The number and complexity of obesity treatments has increased rapidly in recent years. This is driven by the approval of new anti-obesity medications (AOMs) that produce larger degrees of weight loss than previously approved AOMs. Unfortunately, access to these highly effective therapies and to integrated team-based obesity care is limited by intra-/interpersonal patient, institutional/practitioner, community, and policy factors. We contextualized these complexities and the impact of patients’ social drivers of health (SDOH) by adapting the social ecological model for obesity. Without multi-level intervention, these barriers to care will deepen the existing inequities in obesity prevalence and treatment outcomes among historically underserved communities. As General Internal Medicine (GIM) physicians, we can help our patients navigate the complexities of evidence-based obesity treatments. As care team leaders, GIM physicians are well-positioned to (1) improve education for trainees and practitioners, (2) address healthcare-associated weight stigma, (3) advocate for equity in treatment accessibility, and (4) coordinate interdisciplinary teams around non-traditional models of care focused on upstream (e.g., policy changes, insurance coverage, health system culture change, medical education requirements) and downstream (e.g., evidence-based weight management didactics for trainees, using non-stigmatizing language with patients, developing interdisciplinary weight management clinics) strategies to promote optimal obesity care for all patients.

Objective Existing behavioral weight management interventions produce clinically meaningful weight loss. The onset of the COVID‐19 pandemic led to the quick transition of such interventions from in‐person to virtual platforms. This provided a unique opportunity to compare engagement and outcomes for an in‐person versus virtually delivered weight management intervention. Methods A non‐randomized comparison of engagement and weight outcomes was performed between two cohorts who participated in a weight management intervention in person (N = 97) versus three who participated virtually via videoconference (N = 134). Various metrics of engagement were examined, including group class and individual phone call attendance and duration, and retention for weight assessments. Behavioral targets of daily caloric intake and step‐counts and the clinical weight outcome were explored. Results Cohorts (mean [standard deviation] age 47.3 (11.5), 67.1% women: 86.8% White) that participated virtually attended more group sessions (p < 0.001) and had maintenance telephone calls that were of a longer duration (p < 0.001). No other engagement or weight outcomes significantly differed by delivery modality. Conclusions Virtual weight management programs are promising and may generate similar outcomes to those delivered in‐person. Future research should seek to understand how best to promote and sustain engagement in virtual interventions.

Background Partner support is associated with better weight loss outcomes in observational studies, but randomized trials show mixed results for including partners. Unclear is whether teaching communication skills to couples will improve weight loss in a person attempting weight loss (index participant). Purpose To compare the efficacy of a partner-assisted intervention versus participant-only weight management program on 24-month weight loss. Methods This community-based study took place in Madison, WI. Index participants were eligible if they met obesity guideline criteria to receive weight loss counseling, were aged 18–74 years, lived with a partner, and had no medical contraindications to weight loss; partners were aged 18–74 years and not underweight. Couples were randomized 1:1 to a partner-assisted or participant-only intervention. Index participants in both arms received an evidence-based weight management program. In the partner-assisted arm, partners attended half of the intervention sessions, and couples were trained in communication skills. The primary outcome was index participant weight at 24 months, assessed by masked personnel; secondary outcomes were 24-month self-reported caloric intake and average daily steps assessed by an activity tracker. General linear mixed models were used to compare group differences in these outcomes following intent-to-treat principles. Results Among couples assigned to partner-assisted (n = 115) or participant-only intervention (n = 116), most index participants identified as female (67%) and non-Hispanic White (87%). Average baseline age was 47.27 years (SD 11.51 years) and weight was 106.55 kg (SD 19.41 kg). The estimated mean 24-month weight loss was similar in the partner-assisted (2.66 kg) and participant-only arms (2.89 kg) (estimated mean difference, 0.23 kg [95% CI, -1.58, 2.04 kg], p=0.80). There were no differences in 24-month average daily caloric intake (estimated mean difference 50 cal [95% CI: -233, 132 cal], p=0.59) or steps (estimated mean difference 806 steps [95% CI: -1675, 64 steps], p=0.07). The percentage of participants reporting an adverse event with at least possible attribution to the intervention did not differ by arm (partner-assisted: 9%, participant-only, 3%, p = 0.11). Conclusions Partner-assisted and individual weight management interventions led to similar outcomes in index participants. Trial registration Clinicaltrials.gov NCT03801174, January 11, 2019.

Objective biomarkers of food intake are a sought-after goal in nutrition research. Most biomarker development to date has focused on metabolites detected in blood, urine, skin or hair, but detection of consumed foods in stool has also been shown to be possible via DNA sequencing. An additional food macromolecule in stool that harbors sequence information is protein. However, the use of protein as an intake biomarker has only been explored to a very limited extent. Here, we evaluate and compare measurement of residual food-derived DNA and protein in stool as potential biomarkers of intake. We performed a pilot study of DNA sequencing-based metabarcoding (FoodSeq) and mass spectrometry-based metaproteomics in five individuals’ stool sampled in short, longitudinal bursts accompanied by detailed diet records ( n =27 total samples). Dietary data provided by stool DNA, stool protein, and written diet record independently identified a strong within-person dietary signature, identified similar food taxa, and had significantly similar global structure in two of the three pairwise comparisons between measurement techniques (DNA-to-protein and DNA-to-diet record). Metaproteomics identified proteins including myosin, ovalbumin, and beta-lactoglobulin that differentiated food tissue types like beef from dairy and chicken from egg, distinctions that were not possible by DNA alone. Overall, our results lay the groundwork for development of targeted metaproteomic assays for dietary assessment and demonstrate that diverse molecular components of food can be leveraged to study food intake using stool samples.

Background: Partner support is associated with better weight loss outcomes in observational studies, but randomized trials show mixed results for including partners. Unclear is whether teaching communication skills to couples will improve weight loss in index participants. Purpose: To compare the efficacy of a partner-assisted intervention versus participant-only weight management program on long-term weight loss. Methods: This community-based study took place in Madison, WI. Index participants were eligible if they met obesity guideline criteria to receive weight loss counseling, were aged 74 years or younger, lived with a partner, and had no medical contraindications to weight loss; partners were aged 74 years or younger and not underweight. Couples were randomized 1:1 to a partner-assisted or participant-only intervention. Index participants in both arms received an evidence-based weight management program. In the partner-assisted arm, partners attended half of the intervention sessions, and couples were trained in communication skills. The primary outcome was index participant weight at 24 months, assessed by masked personnel; secondary outcomes were 24-month self-reported caloric intake and average daily steps assessed by an activity tracker. General linear mixed models were used to compare group differences in these outcomes following intent-to-treat principles. Results: Among couples assigned to partner-assisted (n=115) or participant-only intervention (n=116), most index participants identified as female (67%) and non-Hispanic White (87%). Average baseline age was 47.27 years (SD 11.51 years) and weight was 106.55 kg (SD 19.41 kg). The estimated mean 24-month weight loss was similar in the partner-assisted (2.66 kg) and participant-only arms (2.89 kg) (estimated mean difference, 0.23 kg [95% CI, -1.58, 2.04 kg]). There were no differences in 24-month average daily caloric intake (50 cal [95% CI: -233, 132 cal]) or steps (806 steps [95% CI: -1675, 64 steps]). The percentage of participants reporting an adverse event with at least possible attribution to the intervention did not differ by arm (partner-assisted: 9%, participant-only, 3%, p=0.11). Conclusions: Partner-assisted and individual weight management interventions led to similar outcomes in index participants. Trial registration: Clinicaltrials.gov NCT03801174

There is a substantial body of clinical evidence supporting the beneficial effects of lower-carbohydrate dietary patterns on multiple established risk factors associated with insulin resistance and cardiovascular diseases in adult populations. Nutrition and health researchers, clinical practitioners, and stakeholders gathered for, “The Scientific Forum on Nutrition, Wellness, and Lower-Carbohydrate Diets: An Evidence- and Equity-Based Approach to Dietary Guidance” to discuss the evidence base around lower-carbohydrate diets, health outcomes, and dietary guidance. Consensus statements were agreed upon to identify current areas of scientific agreement and spotlight gaps in research, education, and practice to help define and prioritize future pathways. Given the evidence base and considering that most American adults are living with at least one nutrition-related chronic disease, there was consensus that including a lower-carbohydrate dietary pattern as one part of the Dietary Guidelines for Americans could help promote health equity among the general population.

In response to the escalating global obesity crisis and its associated health and financial burdens, this paper presents a novel methodology for analyzing longitudinal weight loss data and assessing the effectiveness of financial incentives. Drawing from the Keep It Off trial—a three-arm randomized controlled study with 189 participants—we examined the potential impact of financial incentives on weight loss maintenance. Given that some participants choose not to weigh themselves because of small weight change or weight gains, which is a common phenomenon in many weight-loss studies, traditional methods, for example, the Generalized Estimating Equations (GEE) method tends to overestimate the effect size due to the assumption that data are missing completely at random. To address this challenge, we proposed a framework which can identify evidence of missing not at random and conduct bias correction using the estimating equation derived from pairwise composite likelihood. By analyzing the Keep It Off data, we found that the data in this trial are most likely characterized by non-random missingness. Notably, we also found that the enrollment time (i.e., duration time) would be positively associated with the weight loss maintenance after adjusting for the baseline participant characteristics (e.g., age, sex). Moreover, the lottery-based intervention was found to be more effective in weight loss maintenance compared with the direct payment intervention, though the difference was non-statistically significant. This framework's significance extends beyond weight loss research, offering a semi-parametric approach to assess missing data mechanisms and robustly explore associations between exposures (e.g., financial incentives) and key outcomes (e.g., weight loss maintenance). In essence, the proposed methodology provides a powerful toolkit for analyzing real-world longitudinal data, particularly in scenarios with data missing not at random, enriching comprehension of intricate dataset dynamics.

Background: Weight loss is commonly recommended before total knee arthroplasty (TKA) despite inconsistent evidence for better outcomes. This study sought to examine impacts of preoperative weight loss on patient-reported and adverse outcomes among TKA patients supervised by a medical weight management clinic. Methods: This study retrospectively analyzed patients who underwent medical weight management supervision within 18 months before TKA comparing patients who did and did not have clinically relevant weight loss. Preoperative body mass indices, demographics, Patient-Reported Outcomes Measurement Information System (PROMIS) physical function and pain interference scores, pain intensity scores, and adverse outcomes were extracted. Multivariable linear regressions were performed to determine if preoperative weight loss correlated with patient-reported outcomes after controlling for confounders. Results: There were 90 patients, 75.6% women, who had a mean age of 65 years (range, 42-82) were analyzed. There were 51 (56.7%) patients who underwent clinically relevant weight loss with a mean weight loss of 10.4% and experienced no difference in adverse outcomes. Preoperative weight loss predicted significantly improved 3-months postoperative physical function (β = 15.2 [13.0-17.3], P < 0.001), but not pain interference (β = -18.9 [-57.1-19.4], P=0.215) or pain intensity (β = -1.8 [-4.9-1.2], P=0.222) scores. Conclusion: We found that medically supervised preoperative weight loss predicted improvement in physical function 3 months after TKA. This weight loss caused no major adverse effects. Further research is needed to understand the causal relationships between preoperative weight loss, medical supervision, and outcome after TKA and to elucidate potential longer-term benefits in a larger sample.

Eating a varied diet is a central tenet of good nutrition. Here, we develop a molecular tool to quantify human dietary plant diversity by applying DNA metabarcoding with the chloroplast trnL-P6 marker to 1,029 fecal samples from 324 participants across two interventional feeding studies and three observational cohorts. The number of plant taxa per sample (plant metabarcoding richness or pMR) correlated with recorded intakes in interventional diets and with indices calculated from a food frequency questionnaire in typical diets (ρ = 0.40 to 0.63). In adolescents unable to collect validated dietary survey data, trnL metabarcoding detected 111 plant taxa, with 86 consumed by more than one individual and four (wheat, chocolate, corn, and potato family) consumed by >70% of individuals. Adolescent pMR was associated with age and household income, replicating prior epidemiologic findings. Overall, trnL metabarcoding promises an objective and accurate measure of the number and types of plants consumed that is applicable to diverse human populations.