In many non-Asian countries, soy is consumed via soy-based meat and dairy alternatives, in addition to the traditional Asian soyfoods, such as tofu and miso. Meat alternatives are typically made using concentrated sources of soy protein, such as soy protein isolate (SPI) and soy protein concentrate (SPC). Therefore, these products are classified as ultra-processed foods (UPFs; group 4) according to NOVA, an increasingly widely used food-classification system that classifies all foods into 1 of 4 groups according to the processing they undergo. Furthermore, most soymilks, even those made from whole soybeans, are also classified as UPFs because of the addition of sugars and emulsifiers. Increasingly, recommendations are being made to restrict the consumption of UPFs because their intake is associated with a variety of adverse health outcomes. Critics of UPFs argue these foods are unhealthful for a wide assortment of reasons. Explanations for the proposed adverse effects of UPFs include their high energy density, high glycemic index (GI), hyper-palatability, and low satiety potential. Claims have also been made that UPFs are not sustainably produced. However, this perspective argues that none of the criticisms of UPFs apply to soy-based meat and dairy alternatives when compared with their animal-based counterparts, beef and cow milk, which are classified as unprocessed or minimally processed foods (group 1). Classifying soy-based meat and dairy alternatives as UPFs may hinder their public acceptance, which could detrimentally affect personal and planetary health. In conclusion, the NOVA classification system is simplistic and does not adequately evaluate the nutritional attributes of meat and dairy alternatives based on soy.

Obesity has reached epidemic proportions in the United States. It is implicated in the development of a variety of chronic disease states and is associated with increased levels of inflammation and oxidative stress. The objective of this study is to examine the effect of Medifast's meal replacement program (MD) on body weight, body composition, and biomarkers of inflammation and oxidative stress among obese individuals following a period of weight loss and weight maintenance compared to a an isocaloric, food-based diet (FB).

Soy is a hotly debated and widely discussed topic in the field of nutrition. However, health practitioners may be ill-equipped to counsel clients and patients about the use of soyfoods because of the enormous, and often contradictory, amount of research that has been published over the past 30 years. As interest in plant-based diets increases, there will be increased pressure for practitioners to gain a working knowledge of this area. The purpose of this review is to provide concise literature summaries (400–500 words) along with a short perspective on the current state of knowledge of a wide range of topics related to soy, from the cholesterol-lowering effects of soy protein to the impact of isoflavones on breast cancer risk. In addition to the literature summaries, general background information on soyfoods, soy protein, and isoflavones is provided. This analysis can serve as a tool for health professionals to be used when discussing soyfoods with their clients and patients.

Obesity is a public health crisis, with prevalence rates tripling over the past 60 y. Although lifestyle modifications, such as diet and physical activity, remain the first-line treatments, recent anti-obesity medications (AOMs) have been shown to achieve greater reductions in body weight and fat mass. However, AOMs also reduce fat-free mass, including skeletal muscle, which has been demonstrated to account for 20% to 50% of total weight loss. This can equate to ∼6 kg or 10% of total lean mass after 12–18 mo, a loss comparable to a decade of human aging. Despite questions surrounding the clinical relevance of weight loss-induced muscle loss, the importance of adopting lifestyle behaviors such as eating a protein-rich diet and incorporating regular resistance training to support skeletal muscle health, long-term weight loss maintenance, and overall well-being among AOM users should be encouraged. Herein, we provide a rationale for the clinical significance of minimizing weight-loss-induced lean mass loss and emphasize the integration of diet and physical activity into AOM clinical care. Owing to a lack of published findings on diet and physical activity supporting skeletal muscle health with AOMs, specifically, we lean on findings from large-scale clinical weight loss and diet and exercise trials to draw evidence-based recommendations for strategies to protect skeletal muscle. We conclude by identifying gaps in the literature and emphasizing the need for future experimental research to optimize skeletal muscle and whole-body health through a balance of pharmacotherapy and healthy habits.

Observational research has identified several mortality biomarkers; however, their responsiveness to change is unknown. We tested whether the Healthy Aging Index (HAI) and other mortality biomarkers were responsive to intentional weight loss (WL), which is associated with lower mortality risk in recent meta-analyses. Older adults (70.3 ± 3.7 years) with obesity were randomized into a 6-month WL (n = 47) or weight stability (WS: ±5% baseline weight; n = 48) program. Baseline and 6-month HAI score (0–10) was calculated from component sum (each 0–2: systolic blood pressure, forced vital capacity [FVC], creatinine, fasting blood glucose [FBG], Montreal Cognitive Assessment), and gait speed, grip strength, Digit Symbol Substitution Test, FEV₁, Interleukin-6, C-Reactive Protein, and Cystatin-C were assessed at baseline and 6 months. Mean baseline HAI was 3.2 ± 1.6. By 6 months, WL participants lost 8.87 (95% CI: −10.40, −7.34) kg, whereas WS participants remained weight stable. WL group reduced HAI score (WL: −0.75 [95% CI: −1.11, −0.39] vs WS: −0.22 [95% CI: −0.60, 0.15]; p = .04), and components changing the most were FBG (WL: −3.89 [95% CI: −7.78, 0.00] mg/dL vs WS: 1.45 [95% CI: −2.61, 5.50] mg/dL; p = .047) and FVC (WL: 0.11 [95% CI: −0.01, 0.23] L vs WS: −0.05 [95% CI: −0.17, 0.08] L; p = .06). Among other biomarkers, only Cystatin-C significantly changed (WL: −2.53 [95% CI: −4.38, −0.68] ng/mL vs WS: 0.07 [95% CI: −1.85, 1.98] ng/mL; p = .04). Combining treatment groups, 1 kg WL was associated with a 0.07 (95% CI: 0.03, 0.12) HAI reduction (p < .01). Intentional WL via caloric restriction reduced HAI score by 0.53 points, largely attributable to metabolic and pulmonary improvements.