Análisis Univariado

Obesity is a complex, multifactorial condition. The rise of obesity can be attributed to several major societal and environmental changes (Misra & Khurana, 2008; De Henauw, Verbestel, Marild et al., 2011), encompassed by the term “obesogenic environment” (Stein &

Keller, 2015). Interestingly, the obesogenic environment, depressive symptoms and emotional eating are conceptually related. Emotional eating, the tendency toward overeating in response to negative emotions (Konttinen, Mannisto, Sarlio-Lahteenkorva, Silventoinen & Haukkala, 2010; Goldschmidt, Crosby, Engel et al., 2014), is driven by emotional phenotypes like depression, anxiety, sadness, and boredom, rather than hunger. Ample lines of evidence suggest that chronic high-fat feeding promotes negative emotional states and potentiates condition for enhanced sensitivity to stress that leads to continuous repetitive cycles of overeating, weight gain, and depressed mood (Singh, 2014). Ongoing research shows that chronic exposure to highly processed foods, (filled with empty calories from added sugars and saturated fats), causes drastic changes to the reward circuitry of human brain (Avena, 2015).

Several review articles have been published recently on the biological factors and type of foods that influence appetite and mood via brain signal transduction pathways (Singh, 2014;

Mansur, Brietzke & Mcintyre, 2015; Schulte, Yokum, Potenza & Gearhardt, 2016). The complex nature of food intake where various biological factors link mood, food intake and brain signalling, will not be discussed in great detail here. LEPTIN (LEPR) and GHRELIN (GHRL) are two hormones that have been recognized to have a major influence on energy balance. Both hormones interact with the hypothalamus to regulate food intake, energy

108

homeostasis, promote satiety, and hunger (Spiegelman & Flier, 2001). Interestingly, both hormones have also been associated with the “reward pathway” suggesting the linkage between mood and food intake (Kiefer, Jahn, Kellner, Naber & Wiedemann, 2001; Opland, Leinninger

& Myers, 2010; Dickson, Egecioglu, Landgren et al., 2011). Additionally, the concentration of LEPR in human plasma (Considine, Sinha, Heiman et al., 1996), and its mRNA expression in adipose tissue (Vidal, Auboeuf, De Vos et al., 1996), appear to be directly related to obesity severity. Although, some studies have shown an increased LEPR production in obese individuals who were not leptin-deficient, suggesting that they may not be responsive to leptin signalling (Imam, 2016). Several studies (Caro, Kolaczynski, Nyce et al., 1996; Bjorbaek, El-Haschimi, Frantz & Flier, 1999; Faouzi, Leshan, Bjornholm et al., 2007; Munzberg, Flier &

Bjorbaek, 2004) have attempted to explain the syndrome of leptin resistance. However, apart from several discussions in mutations in the leptin resistance gene (Clément et al., 1998;

Gotoda et al., 1997), the molecular basis of leptin resistance, is yet to be explored (Bakker, van Dielen, Greve, Adam & Buurman, 2004; Montez, Soukas, Asilmaz et al., 2005).

A substantial amount of evidence shows that the dysregulation of the central nervous system (CNS) significantly contributes to obesity and metabolic syndrome. Particularly the disruption of various hypothalamic pathways can be responsible for disordered feeding and energy balance resulting in the development of obesity and related health problems (Cai & Liu, 2011; 2012; Elmquist & Flier, 2004; Flier & Maratos-Flier, 1998; Schwartz & Porte, 2005).

Several neurotransmitter systems have been found to play an important role in feeding behaviour. These include serotonin (Steiger, 2004; Davis et al., 2009)], dopamine (Volkow, Wang & Baler, 2011), and opioids (Davis, Levitan, Reid et al., 2009). The nucleus accumbens, the brain's reward system, receives inputs of endogenous opioids, serotonin, and dopamine and sends outputs to neurons of the hypothalamus that act on appetite control. Several findings have suggested a role of β-endorphin, an opioid neuropeptide, in food intake. It has been suggested

109

that endogenous opioids play a significant role in appetite and metabolism regulation, and thereby control both short- and long-term energy balance (Kim, Lin, Valentino, Colon-Gonzalez & Waldman, 2011). In particular, β-endorphins in the CNS are able to modulate the activity of structures responsible for appetite control and food intake regulation (Bakkali-Kassemi, El Ouezzani, Magoul et al., 2011).

The neurotransmitter release of serotonin in the brain is controlled by food intake (Shabbir, Patel, Mattison et al., 2013). Increased levels of serotonin have been shown to supress feeding behaviour and weight gain (Leibowitz & Alexander, 1998), and chronic dieters have often displayed symptoms of depression due to reduced levels of serotonin (Huether, Zhou, Schmidt, Wiltfang & Rüther, 1997). Interestingly, behaviour linked to serotonergic neurotransmission is influenced by the gut microbiota (O'Mahony, Clarke, Borre, Dinan &

Cryan, 2015), which have also been found to play an important role in energy homeostasis and body weight regulation mechanisms (Rosenbaum, Knight & Leibel, 2015). The essential amino acid tryptophan that comes from high-quality foods (Friedman & Levin, 2012) is the precursor for serotonin synthesis (Prasad, 1998; Richard, Dawes, Mathias et al., 2009). Hence, tryptophan deficiency can lead to lower serotonin levels, although the exact relationship between the central effects of tryptophan depletion/supplementation remains unclear (Crockett, Clark, Roiser et al, 2012; Hughes, Carballedo, McLoughlin et al., 2012; Van Donkelaar, Blokland, Ferrington et al., 2011). On the other hand, consumption of sugar and fat-rich foods leads to increased dopamine turnover, which in the brain produces the same effect as some drugs of abuse, such as cocaine, heroin, methamphetamine etc. (Barry et al., 2009; Fortuna, 2010;

Gluskin & Mickey, 2016).

The amino acid tyrosine is the precursor for dopamine synthesis. It has been shown that the A1 allele of the DOPAMINE 2 RECEPTOR (DRD2) has been implicated in various addictive disorders including alcohol abuse and dependence, cocaine and methamphetamine

110

dependence, bulimia, binge eating disorder and obesity (reviewed in Fortuna, 2010; Gluskin &

Mickey, 2016). DRD2, encodes the D2 subtype of the dopamine receptor, to maintain normal craving behaviours (Del Parigi, Chen, Salbe, Reiman & Tataranni, 2003; Volkow et al., 2011;

Gluskin & Mickey, 2016). The presence of the A1 allele has been suggested as a strong predictor of psychological phenotypes involving a drive for “thinness” and a belief in

“ineffectiveness” (Fortuna, 2010). Compared to their lean counterparts, obese individuals seem to have significantly lower availability of striatal DRD2 in proportion to their BMI (Chen, Lin, Chao et al., 2012a). It has been suggested that dopamine and possibly oxytocin deficiency in obese individuals may perpetuate pathological eating as a means to compensate for decreased activation in motivation and reward circuits modulated by dopamine. It has also been proposed that the association between striatal dopamine and oxytocin receptors, and brain glucose metabolism in somatosensory cortices could underlie one of the mechanisms through which dopamine regulates the reinforcing properties of food (Eisenstein, Gredysa, Antenor-Dorsey et al., 2015; 2016).

The hypothalamo-neurohypophyseal neuropeptide OXYTOCIN (OXT) is classically known for its functions in reproductive physiology of mammalian females (Soloff, Alexandrova & Fernstrom, 1979). However, growing evidence indicates that many of the classical and non-classical actions of OXT are also associated with feeding changes (Arletti, Benelli & Bertolini, 1989; Douglas, Johnstone & Leng, 2007; Leng, Onaka, Caquineau et al., 2008; Ho & Blevins, 2013) with several recent studies linking OXT to the hypothalamus-brain stem circuits that work to inhibit feeding (Baskin, Kim, Gelling et al., 2010; Blevins, Schwartz

& Baskin, 2004; Blouet, Jo, Li & Schwartz, 2009). Additionally, a number of recent studies revealed that obesity can be significantly attributed to OXT release defect, and OXT treatment was able to effectively correct overeating and obesity (Deblon, Veyrat-Durebex, Bourgoin et al., 2011; Maejima, Iwasaki, Yamahara et al., 2011; Zhang & Cai, 2011; Zhang, Zhang, Bai et

111

al., 2011; Zhang, Wu, Chen et al., 2013). (See Cai & Purkayastha, 2013 for a review). It seems that OXT plays a vital role in integrating circadian control with metabolic regulation, suggesting that OXT treatment can amend the circadian dysregulation of metabolic physiology leading to the reduction of obesity (Zhang & Cai, 2011). Furthermore, research demonstrated that when delivered systemically OXT causes reduction in fat mass and adipocyte size (Maejima et al., 2011), whereas deficiency in OXT receptor incurs an opposite effect (Takayanagi, Kasahara, Onaka et al., 2008). A recent clinical trial revealed that OXT treatment in humans not only leads to body weight reduction, but also improves the lipid profile of the patients by lowering serum LOW DENSITY LIPOPROTEIN (LDL) and cholesterol levels and a propensity for HIGH DENSITY LIPOPROTEIN (HDL) level (Zhang et al., 2013). Overall, it seems that OXT can incorporate multiple mechanisms to regulate energy and metabolic homeostasis, and represents a new-generation peptidyl drug target for the treatment of obesity and diabetes (Cai & Purkayastha, 2013).

It has been argued that eating disorders and other addictive behaviours might represent an addiction to hormones related to feeding behaviour (Schellekens, Dinan, & Cryan, 2013), rather than addiction to particular types of food and its hedonic properties. If obesity is modulated by genetic deficiency of particular types of dopamine and endorphin and oxytocin in the CNS, it would be worth investigating whether genes responsible for expression of these, can be changed, altered or transformed. Taken together, these studies imply that certain foods, apart from their effect on obesity, are strong mood regulators.

2.4.2. “-Omics” and the study of obesity and psychological well-being

The Human Genome Project (HGP) has profoundly transformed many scientific fields.

After the publication of the sequence of the human genome (International Human Genome

112

Sequencing Consortium 2001; 2004; Venter, Adams, Myers et al., 2001), the era of “-OMICS”

has emerged to revolutionize the way of studying and learning in medical and biomedical sciences. “OMICS” refers to a field of study ending in the neosuffix “omics” [from Greek -ωμα (-ōma)], such as genomics, transcriptomics, epigenomics, proteomics, metabolomics or interactomics. In contrast to traditional experimental approaches, “-OMICS” are high-throughput, data-driven, holistic, and top-down methodologies, which attempt to understand the cell, tissue, organ or organism phenotype as an integrated system. These high-throughput approaches generate large amounts of data, whereas the analysis of these data often requires significant in-silico efforts and always advanced statistical approaches.

Several reviews have been published on the “-OMIC” technologies recently (Cellerino

& Ori, 2017; Mitra, Carvunis, Ramesh & Ideker, 2013; Valdes, Glass & Spector, 2013;

Brookes & Robinson, 2015; Johnson, Ivanisevic & Siuzdak, 2016), so the “-OMIC”

methodologies will not be described in great detail here. “-OMICS” focus on the dynamic interactions between the different entities of a biological system to analyze networks, pathways, and interactive relations that exist among them, such as genes, transcripts, proteins, metabolites, and/ or cells and cellular structures. Recently, advances in “-OMICS” research fields have come of age and are beginning to be applied in clinical practice. Genomics has, interestingly, garnered great public attention through the HGP.

Genomics, the study of the entirety of an organism’s genes, brings a deluge of data that may lead to the discovery of novel loci associated with psychological well-being and quality of life domains. Genomic variation at the level of nucleotide sequence has been shown that is associated with individual differences in personality and thus with vulnerability and resistance to a wide range of chronic illness and abnormal phenotypes (Ebstein, 2006; Meyer-Lindenberg

& Weinberger, 2006; Rutter, 2007). This has a significant impact in the current understanding of mental health and in the clinical management of patients. With the next generation

113

sequencing technologies (Buermans & Den Dunnen, 2014), a wide range of applications are now affordable. Genomics can also be used to identify molecular networks that are deregulated in emotional and social functioning (Fredrickson, Grewen, Coffey et al., 2013; Slavich & Cole, 2013; Cole, 2014) and mental health (Schizophrenia Working Group of the Psychiatric Genomics, 2014), which will not only elucidate the underlying molecular genetic mechanisms, but may also help to determine the classes of psychological interventions that have to be used for potential treatment.

Global mRNA transcript expression profiling is a very powerful tool in modern biological psychology because it encompasses the organisms’ transcription of activated genes.

Transcriptomics plays several roles in advanced management of human behaviour. Its main applications involve emotion diagnostics based on gene expression profiling of mRNA, as well as biomarker applications in studies of human behaviour. The operation of the genome and transcriptome is regulated by signal transduction pathways that are responsive to environmental experiences. The notion of epigenetics offers a putative interface between the genetic and environmental factors that interact to provide the phenotypic expression (Allis & Jenuwein, 2016). Epigenomics is the systematic analysis of the global state of gene expression not attributable to mutational changes (Stricker, Köferle & Beck, 2017). An organism has multiple, cell type-specific, epigenomes comprising epigenetic marks such as DNA methylation (Suzuki

& Bird, 2008), histone modification (Papamichos-Chronakis & Peterson, 2013), acetylation (Verdin & Ott, 2015), phosphorylation (Humphrey, James & Mann, 2015), ubiquitination (Meas & Mao, 2015), sumoylation (Hendriks & Vertegaal, 2016), non-coding RNAs (Sato, Tsuchiya, Meltzer & Shimizu, 2011) and specifically positioned nucleosomes (Tsankov, Thompson, Socha, Regev & Rando, 2010).

114

2.4.3. Combining molecular genetics with psychological approaches to obesity