*3.3. Correlograms*

Figure 2 presents the correlograms of the correlation matrix between the variables of interest for the obese, overweight, and normal-weight groups separately. The results showed that *HAD Anxiety* and *Depression* scores were significantly positively correlated with the majority of mYFAS symptoms severity among all BMI groups. However, mood and symptom severity scores were more strongly correlated among the obese group than among the other two groups, particularly for *Loss of control* (i.e., *mYFAS\_1*) and *Clinical impairments* (i.e., *YFAS\_9*), with coefficient values around 0.5 for *HAD Anxiety* and 0.6 for *HAD Depression*.

**Figure 2.** Correlograms for each BMI group. (**A**), Correlogram for Obese group (*n* = 57). (**B**), Correlogram for Overweight group (*n* = 147). (**C**), Correlogram for Normal Weight group (*n* = 938). Positive correlations are displayed in blue and negative correlations are displayed in red. The darkness of the color is proportional to the correlation coefficient, such that the strong correlations (i.e., the closest to −1 or 1) are represented in dark boxes. Nonsignificant correlations are displayed in white. HAD Anxiety: Hospital Anxiety and Depression Scale—Anxiety subscale. HAD Depression: Hospital Anxiety and Depression Scale—Depression subscale. EMAQ Positive: Emotional Appetite Questionnaire Positive subscale. EMAQ Negative: Emotional Appetite Questionnaire Negative subscale.

IES-2: Intuitive Eating Scale 2; -EPR: Eating for physical rather than emotional reasons; -RHSC: Reliance on Hunger and Satiety Cues; -UPE: Unconditional Permission to Eat. mYFAS\_1: Loss of control; mYFAS\_2: Cut down; mYFAS\_3: Time spent; mYFAS\_4: Impact activities; mYFAS\_5: Withdrawal; mYFAS\_6: Despite problems; mYFAS\_7: Tolerance; mYFAS\_8: Clinical distress; mYFAS\_9: Clinical impairments.

Unlike the findings among the overweight or normal-weight groups, *EMAQ Negative* scores were significantly positively correlated with the *HAD Anxiety* (*r* = 0.49) and *HAD Depression* scores (*r* = 0.42) among the obese group. Regarding intuitive eating (IES-2), the *Reliance on Hunger and Satiety Cues* (*RHSC*) subscale scores were significantly and negatively correlated with *HAD Anxiety*, *HAD Depression*, and *EMAQ Negative* scores among all BMI groups, but the correlation values were the highest among the obese group (*IES-2 RHSC* and *HAD Anxiety* or *HAD Depression*: *r* = −0.44; *IES-2RHSC* and *EMAQ Negative*: *r* = −0.57).


*Regulation of Iron Metabolism in NAFLD/NASH DOI: http://dx.doi.org/10.5772/intechopen.107221*

(TNFα), and could lead to the release of mitochondrial factors that promote apoptosis [149]. Complete cessation of the mitochondrial electron transport and ATP synthesis is caused by extreme depolarisation of mitochondrial membranes, resulting in cellular necrosis [150]. Because damaged mitochondria cannot efficiently metabolise fatty acids, fatty acids accumulate [151], leading to further hepatic lipid accumulation [152], and promoting inflammatory [153] and fibrogenic responses as well as mitogenic responses that could be carcinogenic [154].

Lipotoxicity induces several different types of cellular stresses, including ER stress [154] and impaired autophagy [155]. In addition, it promotes a sterile inflammatory response that can potentiate liver cell injury and death. At the adipocyte level, metabolic dysregulation because of impaired insulin post-receptor signalling leads to excess lipolysis of triglycerides (TGs) and NEFA release into the circulation. At the molecular level, lipotoxicity leads to endoplasmic reticulum (ER) stress, lysosomal dysfunction, inflammasome activation, cell death and activation of inflammatory responses due to lethal and sublethal hepatocellular injury [156].

NASH occurs because lipotoxic hepatocytes release factors that initiate woundhealing responses to replace dying hepatocytes [157]. Wound healing is a complex multifaceted process that can restore the liver structure and function to a healthy state [158].


**1. Introduction**

Climatic conditions are important for the well-functioning of any ecosystem. Altered climatic conditions have direct effects on plant health, productivity, and yield. Earth's climate is changing very rapidly mainly due to human activities, having negative effects on human and the ecosystem (particularly on the agriculture sector) [1]. Altered climatic conditions contribute negatively due to increased levels of greenhouse gases, global warming, higher emission of CO2, deforestation, and excessive use of fossil fuels. Plants are the primary producers and a very important component of the ecosystem. Temperature fluctuations, particularly due to altered climate conditions, may trigger other factors, such as drought, flood, soil erosion, waterlogging, and salinity, which ultimately led to lower crop productivity and yield. As the world population is increasing rapidly, demand for the food is also increasing. We need good quality and quantity of crops to fulfill the feeding requirements of the world population. Natural stress factors (light intensity, temperature, water stress, and nutrient availability) and anthropogenic stress factors (mainly HM pollution, excessive use of herbicides, acid rain, and enhanced UV-B radiations) contribute strongly to deteriorate crop health and productivity. Continuously changing climatic conditions induce higher stress on crops due to irregular patterns of moisture contents, more pest and disease infection, more waterlogging conditions, increased soil erosion, and global warming [2]. Climate change and food shortage are the most challenging factors of this century, which need our serious efforts and attention. It is very important to develop crops that are better able to tolerate abrupt climate changes and associated abiotic stresses to keep a balance between environment and agricultural crop production [3]. Among other techniques, the plant genetic engineering approach could also be used for abiotic stress management in crops. Transgenic plants have better tolerance level to various kinds of abiotic stress.  Genetic engineering of plants also results in reduced postharvest losses, which improve productivity and yield [4]. Induction of the expression of stress-related TFs (MYC, bzip, DREB1A, DREB1B, DREB1C, CBF1, CBF2), stress-responsive genes, signaling pathway kinases (MAPK, CDPK, S6K, PIP5K) hormonal biosynthesis (ABA, ethylene), antioxidant and ROS scavenging mechanism (APX, GSH, GR, GST, SOD, flavonoids, carotenoids), regulatory proteins (HSPs, LEA, dehydrins, aquaporins, metallothioneins, phytochelatins) osmolytes, and compatible solutes (proline, sorbitol, mannitol, polyamines, amino acids, glycine betaine), transporters (NHX, HKT, HMAs) improve the crop performance under altered environmental conditions. Through genetic engineering and genome editing tools, transgenic plants have developed, which are better able to adapt to climate changes without affecting their productivity and yield. Genotyping, sequencing, transcriptomics, proteomics, metabolomics, and functional genomics can be integrated collectively for the identification of stress-responsive genes/gene products and their expression in targeted plants to develop abiotic stress-tolerant cultivars [3]. Different genome editing tools are being used like CRIPR/Cas9, which is of prime importance due to its rapid and effective outcomes. It is an environment-friendly technique to produce transgenic plants, which are better adapted to stress conditions that emerges due to climate change. The CRISPR system is based on candidate gene knockout/insertion or gene replacement, which results in either loss of function, downregulated, or over-expression of gene for abiotic stress tolerance [5].


**Author details**

Luis Jesús Villarreal-Gómez1\*, Irma Esthela Soria-Mercado2 , Ana Leticia Iglesias3 and Graciela Lizeth Perez-Gonzalez3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Center of Engineering and Technology, University Autonomous of Baja California, Tijua‐ na, BC., México

2 Marine Science Faculty, University Autonomous of Baja California, Ensenada, BC., México

3 Center of Engineering and Technology, University Autonomous of Baja California, Tijua‐ na, BC., México