Illuminating the 'healthy obese' phenotype

Association of the visceral fat metabolic score with osteoarthritis risk: a cross-sectional study from NHANES 2009-2018

BACKGROUND

Currently, obesity has been recognized to be an independent risk factor for osteoarthritis (OA), and the Metabolic Score for Visceral Fat (METS-VF) has been suggested to be potentially more accurate than body mass index (BMI) in the assessment of obesity. Nevertheless, the correlation of METS-VF with OA has not been obviously revealed yet. Therefore, this study aimed to delve into the potential relationship between METS-VF and OA.

METHODS

By examining data from the NHANES (2009-2018), weighted multivariate logistic regression analyses were used for assessing the correlation between METS-VF and OA. Subgroup analyses were then performed to validate the findings. Moreover, the nonlinear relationship between the two was assessed by restricted cubic spline (RCS). Receiver operating characteristic (ROC) curves were plotted to examine the diagnostic accuracy of METS-VF versus previous obesity index for OA.

RESULTS

This study involved 7639 participants. According to our results, METS-VF was notably related to an elevated risk of OA, regardless of the METS-VF and the trend of positive association was more pronounced with the elevating METS-VF level (p for trend < 0.05). Subgroup analyses showed that the positive association between METS-VF and prevalence of osteoarthritis persisted in all populations with different characteristics, confirming its validity in all populations. Besides, RCS results showed a significant non-linear relationship between METS-VF and OA (p-non-linear < 0.05). As indicated by the ROC curve analysis results, METS-VF was a superior predictor of OA to BMI and HC.

CONCLUSIONS

This study finds a possible nonlinear positive correlation between METS-VF and the risk of OA. In addition, METS-VF may serve as an indicator for the more accurate diagnosis of OA and provide a new way to further evaluate the relationship between visceral fat and OA.

Obesity and body mass index: Past and future considerations in osteoarthritis research

Obesity is an important topic for the osteoarthritis (OA) scientific community. However, the predominant use of body mass index (BMI) to define obesity in OA research is associated with uncertainties and limitations. These include an inability to discern fat and muscle mass, account for sex-differences in fat distribution, or identify adiposity-related health impairments. A focus on BMI in OA research may influence weight bias in clinical practice and impact disparities in access to effective OA treatments. To ensure that our understanding and approaches to improve health outcomes for individuals with or at risk for OA continues to advance in the next decade, future research will need to consider alternative measures beyond BMI for obesity identification and align with evolving obesity science. OA researchers must be aware of issues associated with weight stigma and work to minimize negative generalizations based on BMI.

Fat as a Friend or Foe of the Bone

PURPOSE OF REVIEW

The objective of this review is to summarize the literature on the prevalence and diagnosis of obesity and its metabolic profile, including bone metabolism, focusing on the main inflammatory and turnover bone mediators that better characterize metabolically healthy obesity phenotype, and to summarize the therapeutic interventions for obesity with their effects on bone health.

RECENT FINDINGS

Osteoporosis and fracture risk not only increase with age and menopause but also with metabolic diseases, such as diabetes mellitus. Thus, patients with high BMI may have a higher bone fragility and fracture risk. However, some obese individuals with healthy metabolic profiles seem to be less at risk of bone fracture. Obesity has become an alarming disease with growing prevalence and multiple metabolic comorbidities, resulting in a significant burden on healthcare and increased mortality. The imbalance between increased food ingestion and decreased energy expenditure leads to pathological adipose tissue distribution and function, with increased secretion of proinflammatory markers and harmful consequences for body tissues, including bone tissue. However, some obese individuals seem to have a healthy metabolic profile and may not develop cardiometabolic disease during their lives. This healthy metabolic profile also benefits bone turnover and is associated with lower fracture risk.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Diet and obesity effects on cue-driven food-seeking: insights from studies of Pavlovian-instrumental transfer in rodents and humans

Our modern environment is said to be obesogenic, promoting the consumption of calorically dense foods and reducing energy expenditure. One factor thought to drive excess energy intake is the abundance of cues signaling the availability of highly palatable foods. Indeed, these cues exert powerful influences over food-related decision-making. Although obesity is associated with changes to several cognitive domains, the specific role of cues in producing this shift and on decision-making more generally, remains poorly understood. Here we review the literature examining how obesity and palatable diets affect the ability of Pavlovian cues to influence instrumental food-seeking behaviors by examining rodent and human studies incorporating Pavlovian-instrumental transfer (PIT) protocols. There are two types of PIT: (a) general PIT that tests whether cues can energize actions elicited in the pursuit of food generally, and (b) specific PIT which tests whether cues can elicit an action that earns a specific food outcome when faced with a choice. Both types of PIT have been shown to be vulnerable to alterations as a result of changes to diet and obesity. However, effects appear to be driven less by increases in body fat and more by palatable diet exposure per se. We discuss the limitations and implications of the current findings. The challenges for future research are to uncover the mechanisms underlying these alterations to PIT, which appear unrelated to excess weight itself, and to better model the complex determinants of food choice in humans.