Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm
Graphical abstract
Introduction
Obesity has been recognized by the World Health Organization (WHO) as the epidemic of the 21st century due to the alarming increase in its incidence worldwide and its impact on the morbidity and mortality, that threatens to overwhelm the healthcare systems (World Health Organization, 2016). This multifactorial disorder results from the interaction among a plethora of factors, including genetic and environmental ones, with special focus on the Westernized dietary patterns and the sedentary lifestyle (De Los Reyes-Gavilán, Delzenne, González, Gueimonde, & Salazar, 2014). The long-term excessive caloric intake promotes adipose tissue inflammation (DeMarco, Aroor, & Sowers, 2014; Emanuela et al., 2012; Trayhurn, 2005), leading to ectopic lipid accumulation (Cavalcante-Silva, Galvão, da Silva, de Sales-Neto, & Rodrigues-Mascarenhas, 2015). Despite this understanding of the underlying factors, after more than two decades of research, there is not still a clear conclusion about the role of free fatty acids (FFA) metabolism in obesity. It is generally acknowledged that the concentration of circulating FFA is increased in obesity and that high levels of FFA are implicated in the pathogenesis of obesity-related insulin resistance, type 2 diabetes and cardiovascular diseases (Karpe, Dickmann, & Frayn, 2011). However, a recent meta-analysis compiling results from 43 studies has reported only normal or moderately increased levels of FFA in obesity (Karpe et al., 2011). Considering that plasma FFA concentrations are mainly produced by the breakdown of intracellular triglycerides into fatty acids, it is reasonable to expect a large degree of variation in the fatty acid profile as depending on the composition of the subject's diet. Despite that both, saturated and polyunsaturated fatty acids, contribute to the increase in FFA (Lee et al., 2006) and provide similar energy content, they could have a different behavior from a metabolic point of view and, as a consequence, a differential role in obesity.
Several authors have proposed that different fatty acids are able to drive different changes in the composition and functionality of the intestinal microbiota, thus contributing to host lipid metabolism (Rodríguez-Carrio et al., 2017), to the development of obesity (Karlsson et al., 2013; Khan, Nieuwdorp, & Bäckhed, 2014; Le Chatelier et al., 2013; Tremaroli & Bäckhed, 2012; Zhao, 2013), and to the higher proinflammatory status classically associated to subjects with this pathology (Clarke et al., 2014; Rodríguez-Carrio et al., 2017). Results from intervention studies in animals and humans have reported changes in certain intestinal microbial populations in the context of obesity although, the specific mechanisms that link rearrangements of the gut microbial composition to the pathogenesis of obesity and related metabolic diseases remain mostly unexplored (Dao and Clément, 2018). Although several authors have found an increase in the ratio Firmicutes/Bacteroidetes, these results remain controversial. Some Bifidobacterium and Lactobacillus species as well as Akkermansia muciniphila have been associated with lean phenotype (Dao et al., 2016; Million et al., 2012). Other microorganisms such as Faecalibacterium prausnitzii showed inconsistent results about its role in obesity (Feng et al., 2014). In this context, the aim of this study was to analyze the role of FFA in obesity and to identify possible serum FFA profile/s associated with this condition, as well as the role of the gut microbiota in this relationship. Since the traditional recommendations based on weight loss through diet modification and exercise have not been successful enough to fight against obesity, the identification of possible serum FFA profiles associated with obesity could open new dietary or pharmacological ways to normalize serum FFA levels and improve the response of the obese people to treatments.
Section snippets
Participants
The study sample comprised 66 adult volunteers, 26 men and 40 women, aged from 19 to 67 years (mean ± SD, 52.7 ± 11.2) and with a BMI ranging from 19.0 to 40.0 kg/m2, that were recruited in Asturias Region (Northern Spain). In a personal interview, volunteers were informed of the objectives of the study and those deciding to participate gave their fully informed written consent. Subjects were initially classified according to their Body Mass Index (BMI) (Salas-Salvado, Rubio, Barbany, & Moreno,
Results
The general characteristics of the sample, classified according to BMI, are presented in Table 3. Pre-obese and obese subjects (BMI 27.0–40.0 kg/m2) showed higher percentage of fat mass, serum leptin and CRP. The proportion of females in groups decreased as BMI increased.
To investigate whether serum FFA levels were associated to obesity, we determined total (mM) and individual (μg/mL) fasting circulating FFA levels. Our data showed a serum FFA profile linked to obesity that was characterized by
Discussion
The present work represents a preliminary study to investigate the potential relationship between serum FFA as indicator of dietary fat and the gut microbiota. Identification of novel lipid predictors raises the possibility to improve our understanding of the obesity and associated metabolic changes.
We have identified an obese FFA profile, characterized by the simultaneous reduction of EPA serum levels and increased concentration of linoleic, gamma-linolenic and palmitic acids, as regards of
Conclusions
These results reveal the different pattern of serum fatty acids as potential predictors of obesity, supporting the pivotal role of the gut microbiota in this complex interrelationship. The underlying mechanisms explaining these associations should be investigated in future intervention studies that could provide new hypotheses to reduce the incidence of obesity and to develop optimal strategies for early prevention and detection of related disorders.
Acknowledgments
We show our greatest gratitude to all the volunteers participating in the study.
Funding
This work was funded through the project GRUPIN14-043 “Microbiota Humana, Alimentación y Salud” granted by “Plan Regional de Investigación del Principado de Asturias”, Spain, and by the Alimerka Foundation. NS is the recipient of a postdoctoral contract awarded by the Fundación para la Investigación Biosanitaria de Asturias (FINBA). Public National and Regional grants received cofounding from European Union FEDER funds.
Author contributions
S. González had the primary responsibility in the study design and protocol development and confirms that she had full access to the data in the study and final responsibility for the decision to submit for publication. Together with T. Fernández-Navarro they contributed to data analysis and writing of the manuscript. I. Díaz performed and developed the machine learning algorithm and contributed to the writing of the paper. I. Gutiérrez-Díaz was involved in data collection and performed data
Declarations of interest
None.
References (51)
- et al.
Omega-3 fatty acids in obesity and metabolic syndrome: A mechanistic update
The Journal of Nutritional Biochemistry
(2018) Obesity and free fatty acids
Endocrinology and Metabolism Clinics of North America
(2008)- et al.
Gut microbiota and obesity: Concepts relevant to clinical care
European Journal of Internal Medicine
(2018) - et al.
The impact of nutrition on intestinal bacterial communities
Current Opinion in Microbiology
(2017) - et al.
Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans
The American Journal of Clinical Nutrition
(2011) - et al.
Microbial modulation of insulin sensitivity
Cell Metabolism
(2014) - et al.
SEEDO 2007, Consensus for the evaluation of overweight and obesity and the establishment of therapeutic intervention criteria
Medicina Clínica
(2007) - et al.
Fatty acids from diet and microbiota regulate energy metabolism
F1000Research
(2015) - et al.
Serum fatty acids, desaturase activities and abdominal obesity - a population-based study of 60-year old men and women
PLoS One
(2017) - et al.
Establishment and development of intestinal microbiota in preterm neonates
FEMS Microbiology Ecology
(2012)
Gender differences in fat metabolism
Current Opinion in Clinical Nutrition and Metabolic Care
Association between faecalibacterium prausnitzii reduction and inflammatory bowel disease: A meta-analysis and systematic review of the literature
Gastroenterology Research and Practice
Obesity-driven gut microbiota inflammatory pathways to metabolic syndrome
Frontiers in Physiology
Minireview: Gut microbiota: The neglected endocrine organ
Molecular Endocrinology
Impact of omega-3 fatty acids on the gut microbiota
International Journal of Molecular Sciences
Association of polyphenols from oranges and apples with specific intestinal microorganisms in systemic lupus erythematosus patients
Nutrients
Pilot study of diet and microbiota: Interactive associations of fibers and polyphenols with human intestinal bacteria
Journal of Agricultural and Food Chemistry
Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: Relationship with gut microbiome richness and ecology
Gut
Development of functional foods to fight against obesity - Opportunities for probiotics and prebiotics
Agro Food Industry Hi-Tech
The pathophysiology of hypertension in patients with obesity
Nature Reviews. Endocrinology
Replication of obesity and associated signaling pathways through transfer of microbiota from obese-prone rats
Diabetes
Inflammation as a link between obesity and metabolic syndrome
Journal of Nutrition and Metabolism.
The abundance of fecal Faecalibacterium prausnitzii in relation to obesity and gender in Chinese adults
Arch Microbiol
Differential adaptation of human gut microbiota to bariatric surgery-induced weight loss: Links with metabolic and low-grade inflammation markers
Diabetes
Increased oxidative stress in obesity and its impact on metabolic syndrome
The Journal of Clinical Investigation
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