The metabolic nature of individuality

Cognitive-Psychological Characteristics Influencing Weight Loss in Severe Obesity

Background/Objectives: Cognitive and psychological factors, such as eating disorder psychopathology, irrational food beliefs, and internalized weight stigma, have not been sufficiently explored in the context of obesity treatment. This study evaluated the role of these variables as predictors of weight loss in patients with severe obesity following a brief intensive cognitive-behavioral therapy for obesity (CBT-OB) program. Methods: A total of 400 patients (mean BMI: 41.9 kg/m2; mean age: 55.9 years) participated in a 21-day residential CBT-OB intervention, followed by a 12-month follow-up assessment. The Eating Disorder Examination Questionnaire, Weight Bias Internalization Scale, and Irrational Food Beliefs Scale were administered at admission and discharge. Body weight was also assessed at the 12-month follow-up. Results: Of the participants, 371 patients (92.2%) completed the intensive CBT-OB program, and 310 (81.1%) attended the follow-up. On average, completers achieved 9% weight loss at follow-up, accompanied by a significant reduction in binge-eating episodes. Cognitive factors, including lower baseline eating concern, higher baseline weight concern, and greater improvement in irrational beliefs (specifically self-deception about eating and weight control), significantly predicted weight loss at 12 months. However, internalized weight stigma did not predict weight loss in this cohort. Conclusions: This study underscores the importance of targeting specific cognitive factors in obesity treatment to enhance long-term outcomes. Addressing irrational food beliefs and promoting flexible dietary restraint may improve weight loss and maintenance in individuals with severe obesity. Further research is warranted to refine cognitive-behavioral interventions for personalized obesity management strategies.

Towards nutrition with precision: unlocking biomarkers as dietary assessment tools

Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

Behavioral and Psychological Factors Affecting Weight Loss Success

PURPOSE OF REVIEW

There is a large variability between individuals in the weight loss response to any given diet treatment, which fuels interest into personalized or precision nutrition. Although most efforts are directed toward identifying biological or metabolic factors, several behavioral and psychological factors can also be responsible for some of this interindividual variability.

RECENT FINDINGS

There are many factors that can influence the response to dietary weight loss interventions, including factors related to eating behavior (emotional eating, disinhibition, restraint, perceived stress), behaviors and societal norms related to age and sex, psychological and personal factors (motivation, self-efficacy, locus of control, self-concept), and major life events. The success of a weight loss intervention can be influenced by many psychological and behavioral constructs and not merely by physiological factors such as biology and genetics. These factors are difficult to capture accurately and are often overlooked. Future weight loss studies should consider assessing such factors to better understand the underlying reasons for the large interindividual variability to weight loss therapy.

Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach

Background and aim

Results from randomized controlled trials indicate that no single diet performs better than other for all people living with obesity. Regardless of the diet plan, there is always large inter-individual variability in weight changes, with some individuals losing weight and some not losing or even gaining weight. This raises the possibility that, for different individuals, the optimal diet for successful weight loss may differ. The current study utilized machine learning to build a predictive model for successful weight loss in subjects with overweight or obesity on a New Nordic Diet (NND).

Methods

Ninety-one subjects consumed an NND ad libitum for 26 weeks. Based on their weight loss, individuals were classified as responders (weight loss ≥5%, n = 46) or non-responders (weight loss <2%, n = 24). We used clinical baseline data combined with baseline urine and plasma untargeted metabolomics data from two different analytical platforms, resulting in a data set including 2,766 features, and employed symbolic regression (QLattice) to develop a predictive model for weight loss success.

Results

There were no differences in clinical parameters at baseline between responders and non-responders, except age (47 ± 13 vs. 39 ± 11 years, respectively, p = 0.009). The final predictive model for weight loss contained adipic acid and argininic acid from urine (both metabolites were found at lower levels in responders) and generalized from the training (AUC 0.88) to the test set (AUC 0.81). Responders were also able to maintain a weight loss of 4.3% in a 12 month follow-up period.

Conclusion

We identified a model containing two metabolites that were able to predict the likelihood of achieving a clinically significant weight loss on an ad libitum NND. This work demonstrates that models based on an untargeted multi-platform metabolomics approach can be used to optimize precision dietary treatment for obesity.

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

PURPOSE OF REVIEW

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes - based on circulating biomarkers - that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

RECENT FINDINGS

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials. Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.

Protein Intake and Frailty: A Matter of Quantity, Quality, and Timing

Frailty is a geriatric syndrome that refers to a state of reduced resiliency to stressful events that occurs in response to physiological and/or psychosocial detriments. Frailty is a predictor of poor prognosis, given that frail older adults are at higher risk of many adverse health-related events. Hence, the identification of potential strategies to prevent the development and progression of frailty is of extreme importance for avoiding its negative outcomes. An adequate protein consumption is advocated as a possible intervention for the management of frailty in older adults due to its effects on muscle mass and physical function. However, empirical evidence is still needed to support this proposition. On the other hand, substantial evidence from observational studies has provided important information on the association between frailty and dietary protein-related parameters. Here, we provide a narrative review of the current literature regarding the association between protein intake (amount (how much?), quality (what type?), and distribution across meals (when?)) and frailty-related parameters. The ultimate aim of this work is to offer practical, evidence-based indications to healthcare professionals responsible for the care of frail older adults.