Proteomic and Metabolomic Characterization of Metabolically Healthy Obesity: A Descriptive Study from a Swedish Cohort

The Role of Inflammatory Mediators in the Pathogenesis of Obesity

Obesity is a pandemic of the 21st century, and the prevalence of this metabolic condition has enormously increased over the past few decades. Obesity is associated with a number of comorbidities and complications, such as diabetes and cardiovascular disorders, which can be associated with severe and fatal outcomes. Adipose tissue is an endocrine organ that secretes numerous molecules and proteins that are capable of modifying immune responses. The progression of obesity is associated with adipose tissue dysfunction, which is characterised by enhanced inflammation and apoptosis. Increased fat-tissue mass is associated with the dysregulated secretion of substances by adipocytes, which leads to metabolic alterations. Importantly, the adipose tissue contains immune cells, the profile of which changes with the progression of obesity. For instance, increasing fat mass enhances the presence of the pro-inflammatory variants of macrophages, major sources of tumour necrosis factor α and other inflammatory mediators that promote insulin resistance. The pathogenesis of obesity is complex, and understanding the pathophysiological mechanisms that are involved may provide novel treatment methods that could prevent the development of serious complications. The aim of this review is to discuss current evidence describing the involvement of various inflammatory mediators in the pathogenesis of obesity.

Exposure to hand-held vibrating tools and biomarkers of nerve injury in plasma: a population-based, observational study

OBJECTIVES

To analyse potential biomarkers for vibration-induced nerve damage in a population-based, observational study.

DESIGN

Prospective cohort study.

SETTING

Malmö Diet Cancer Study (MDCS), Malmö, Sweden.

PARTICIPANTS

In a subcohort of 3898 individuals (recruited 1991-1996) from MDCS (baseline examination in 28 449 individuals; collection of fasting blood samples in a cardiovascular subcohort of MDCS of 5540 subjects), neuropathy-relevant plasma biomarkers were analysed during follow-up after filling out questionnaires, including a question whether work involved hand-held vibrating tools, graded as 'not at all', 'some' or 'much'.

PRIMARY OUTCOME MEASURES

The neuropathy-relevant plasma biomarkers vascular endothelial growth factor (VEGF)-A, VEGF-D, VEGF receptor 2, galanin, galectin-3, HSP27, ß-nerve growth factor, caspase-3, caspase-8, transforming growth factor-α and tumour necrosis factor were analysed. Data were analysed by conventional statistics (Kruskal-Wallis test; post hoc test Mann-Whitney U test; Bonferroni correction for multiple testing) and in a subanalysis for galanin using two linear regression models (unadjusted and adjusted).

RESULTS

Among participants, 3361 of 3898 (86%) reported no work with hand-held vibrating tools, 351 of 3898 (9%) reported some and 186 of 3898 (5%) much work. There were more men and smokers in vibration-exposed groups. Galanin levels were higher after much vibration exposure (arbitrary units 5.16±0.71) compared with no vibration exposure (5.01±0.76; p=0.015) with no other observed differences.

CONCLUSIONS

Higher plasma levels of galanin, possibly related to magnitude, frequency, acceleration and duration, as well as to severity of symptoms of vibration exposure, may be found in individuals working with hand-held vibrating tools.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Galectin-4 levels in hospitalized versus non-hospitalized subjects with obesity: the Malmö Preventive Project

BACKGROUND

Obesity is strongly associated with the development of cardiovascular disease (CVD). However, the heterogenous nature of obesity in CVD-risk is still poorly understood. We aimed to explore novel CVD biomarkers and their possible association with presumed unhealthy obesity, defined as hospitalized subjects with obesity (HO).

METHODS

Ninety-two proteins associated with CVD were analyzed in 517 (mean age 67 ± 6 years; 33.7% women) individuals with obesity (BMI ≥30 kg/m²) from the Malmö Preventive Project cohort, using a proximity extension array technique from the Olink CVD III panel. Individuals with at least one recorded hospitalization for somatic disease prior to study baseline were defined as HO phenotypes. Associations between proteins and HO (n = 407) versus non-hospitalized subjects with obesity (NHO, n = 110), were analyzed using multivariable binary logistic regression, adjusted for traditional risk factors.

RESULTS

Of 92 analyzed unadjusted associations between biomarkers and HO, increased levels of two proteins were significant at a false discovery rate < 0.05: Galectin-4 (Gal-4) and insulin-like growth factor-binding protein 1 (IGFBP-1). When these two proteins were included in logistic regression analyses adjusted for age and sex, Gal-4 remained significant. Gal-4 was independently associated with the HO phenotype in multivariable logistic regression analysis (OR 1.72; CI95% 1.16-2.54). Post-hoc analysis revealed that this association was only present in the subpopulation with diabetes (OR 2.26; CI95% 1.25-4.07). However, an interaction analysis was performed, showing no significant interaction between Gal-4 and prevalent diabetes (p = 0.16).

CONCLUSIONS

In middle-aged and older individuals with obesity, increased Gal-4 levels were associated with a higher probability of HO. This association was only significant in subjects with diabetes only, further implying a role for Gal-4 in diabetes and its complications.