Endocrine system by Dr. Paul Cottrell

Endocrine Systems


Vivien Cheng

Dr. Paul Cottrell

John Mark Johnson

Amrit Sanal


Human Endocrine Physiology

October 18, 2018

1. Introduction 


The endocrine system assumes a primary role for maintaining energy homeostasis within the body. In coordination with the nervous system, the endocrine system is responsible for regulating hormonal responses to control the use and storage of energy molecules in order to meet the physiological demands of the body and ensure proper cellular functioning. These actions are essential for human survival as they allocate the energy necessary to operate in ever changing environments: supplying the brain with enough glucose to think and perceive, to digest nutrients, to ready the body to external threats (i.e. fight-or-flight), to reproduce, etc. The goal of this paper is to discuss a few of the main components of the endocrine system that are crucial for homeostasis of energy metabolism: insulin, adipose tissue, as well as the roles of gastrointestinal and neuropeptide hormones. In this discussion, we will review the mechanisms behind each component and explore how they affect overall energy metabolism. Furthermore, we will delve into the effects of exercise on the endocrine system and review the mechanisms influencing hormone levels. Finally, we will discuss recent research that presents a new outlook on exercise by way of an evolutionary renovation of metabolic mechanisms that makes humans distinct from our ape ancestors. 


2. Insulin


Insulin is one of the most important hormones in human physiology. Beta-cells of the pancreatic islets of Langerhans act as glucose sensors, adjusting insulin output to the prevailing blood glucose level. Insulin is released as the beta cells recognize that there are higher levels of glucose in the bloodstream, and then goes on to act to bring blood glucose levels back within the normal range. Insulin does this in two ways, by increasing the uptake of glucose into skeletal muscle and by stimulating the production of glycogen in the liver. Insulin stimulates glucose uptake in skeletal muscle by promoting the membrane translocation of GLUT4, the major glucose transporter in skeletal muscle [1]. In the liver, insulin acts to modulate the hepatic output of glucose [...] by limiting the production and secretion of glucose from the liver [through]  inhibition of glucagon secretion, reduction of levels of free fatty acids, reduction of gluconeogenic precursors, and changes in neural signaling relayed to the liver [2]. Insulin activates the IR in the liver, [which leads] to the activation of PI3K and ultimately Akt2. The activation of Akt2 promotes glycogen synthesis and inhibits gluconeogenesis and glucose production. [1][3]. Insulin thereby keeps the glucose levels in the blood within homeostatic levels, which in turn decreases the metabolic rate so that there will be more stores of glucose for the future. 

Insulin secretion, as mentioned before is controlled by the beta cells of the islet of Langerhans. Insulin secretion is thought to be stimulated by four different pathways. One way is through glucose sensing and insulin secretion. How glucose is sensed by the cell is still an area of study that has no conclusive answer, but what’s generally agreed upon is that the glucose sensing step is within the actions of metabolism within the beta cell. The secretion of insulin is induced also by actions of metabolism. The generation of ATP in the beta cell leads to the closure of ATP-sensitive K+ (KATP) channels which leads to depolarization of the membrane. This depolarization opens voltage gated Ca 2+ channels, which induces insulin granule exocytosis [4]. The second way that insulin secretion is stimulated is through incretins. Incretins are hormones of the GI tract that amplifies the insulin response to glucose. The major incretins include glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). The actions of incretins provide a feedforward component to glucose regulation during the ingestion of a meal [5]. The third stimulus of insulin secretion is through amino acid metabolism. Several amino acids are known to elicit positive and/or negative effects on β-cellinsulin release in vitro and in vivo. [6] Amino acids tend to influence secretion of insulin similarly to how glucose stimulate insulin secretion. The fourth way insulin secretion is stimulated is through autonomic neuronal stimulation, which depolarizes the membrane during situations of stress. 

Epinephrine, cortisol and growth hormone are all hormones that are released during hypoglycemia and other stress situations. These hormones all have insulin antagonistic effects in the liver and other peripheral tissues. Epinephrine is a fast acting antagonist to insulin, whereas cortisol and growth hormone are for more prolonged situations [7]. These hormones become physiologically important when the body is under stressful conditions. Epinephrine acts in the liver to promote glycogen decomposition into glucose while cortisol and growth hormone act to break down fats and proteins into glucose for long term energy usage. In a stressful situation, the body needs to have more energy at hand, and these hormones effectively counter the effect of insulin to provide the body with the energy it needs. The incretins provide a similar, but opposite effect as these other hormones. Incretins provide a feedforward, amplifying effect to insulin, both preparing the body for an intake of glucose, and making the insulin response more widespread and effective on glucose metabolism. The incretins also provide a check to the stress response of epinephrine, cortisol and GH, making it sure that there are still some energy reserves for the body. 

The insulin receptor belongs to the receptor tyrosine kinase superfamily. Insulin binds to two distinct sites on each subunit of the receptor, crosslinking the two receptor halves to create high affinity [8]. The tyrosine kinases that are attached to the insulin receptor work to phosphorylate and activate certain proteins which eventually lead to the cascade of effects which lead to glucose uptake by the cell or glycogen synthesis. 


1.   Zhang, J. and Liu, F. (2014), Tissue‐specific insulin signaling in the regulation of metabolism and aging. IUBMB Life, 66: 485-495. doi:10.1002/iub.1293

2.   Sharma, M. D., Garber, A. J., and Farmer, J. A. (2008) Role of insulin signaling in maintaining energy homeostasis. Endocr. Pract.14, 373–380.

3.   Zhang, J. and Liu, F. (2014), Tissue‐specific insulin signaling in the regulation of metabolism and aging. IUBMB Life, 66: 485-495. doi:10.1002/iub.1293  

4.   MacDonald P.E., Joseph J.W., Rorsman P. Glucose-sensing mechanisms in pancreatic β-cells. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2005;360:2211–2225. doi: 10.1098/rstb.2005.1762. 

5.   Widmaier, E. P., Raff, H., & Strang, K. T. (2016). Vanders human physiology: The mechanisms of body function(14th ed.). New York, NY: McGraw-Hill Education.

6.   Keane, K. and Newsholme, P. (2014) Metabolic regulation of insulin secretion. Vitam. Horm. 95, 1–33

7.   Lager I. The insulin-antagonistic effect of the counterregulatory hormones. J Inter Med Suppl1991; 735:41–47. 

8.   De Groot LJ, Chrousos G, Dungan K, et al., editors. South Dartmouth (MA): MDText.com, Inc.; 2000-.


3. Adipose Tissue


The cells that secrete leptin and adiponectin are adipocytes.   Adipocytes can be found in white adipose tissue (WAT), brown adipose tissue (BAT), and bone marrow adipose tissue (MAT). Leptin regulates energy balance by inhibiting hunger through opposition to the actions of the hormone ghrelin. Adiponectin are involved in regulating glucose levels and fatty acid breakdown. The leptin receptor is a transmembrane-domain receptor that in hypothalamic neurons regulate energy homeostasis by activating the STAT3 pathway [1].  The adiponectin receptors are AdipoR1 and AdipoR2, which are structurally and functionally distinct from G-protein-coupled receptors [2]. Adiponectin receptors activates the transduction pathway of AMP-activated protein kinase for energy balance regulation [2].

            To maintain long-term energy homeostasis leptin expression and secretion are elevated during energy absorption [1].  When energy intake exceeds energy expenditures fat deposition results, which releases higher levels of leptin into the plasma.  Leptin then signals the hypothalamus to inhibit hunger through the neurotransmitter neuropeptide Y and suppression of ghrelin hormones. This inhibition of hunger signal from leptin lowers energy intake and increases metabolic rate. This negative feedback loop in extremely important in long-term maintenance of the energy intake and energy expenditure balance. The short-term effects of higher plasma levels of leptin are to suppress hunger, which helps to control insulin secretion during absorption episodes—reducing glucose uptake by cells.  

            To maintain long-term energy homeostasis adiponectin is secreted from adipocytes, which is also known to have antidiabetic, antiatherogenic, anti-inflammatory, and angiogenic properties [2].  A short-term effect of adiponectin secretion is the effects on muscle tissue, whereby it stimulates glucose transport by increasing GLUT4 translocation—leading to increased energy expenditures [2].  Another short-term effect of adiponectin secretion into plasma is to lower the production of glucose from hepatic cells [3].  In normal individuals to maintain glucose and fatty acid levels adiponectin is a negative feedback on glucose production and a positive feedback on fatty acid oxidation.

            Leptin and adiponectin effects the reproductive axis as well.  Leptin has roles in puberty and pregnancy.  It has been found that leptin deficient individuals have reduced puberty development unless treated with external sources of leptin via direct and indirect regulation of GnRH [4].  Lower GnRH due to lower leptin levels leads to reproductive and sexual development issues.  Interestingly, it has been shown that leptin can induce ovulation in GnRH deficient mice [4].  Pregnant women secret elevated levels of leptin for the placenta into maternal plasma circulation resulting in leptin resistance in pregnancy—allowing a new setpoint for body weight and increased food intake levels to help with the fetal growth [4].  Adiponectin in plasma positively correlates with the number of oocytes retrieved in FSH treatment for superovulation [2].  Fetal-maternal interface is also important to regulate, which is accomplished by adiponectin controlling endometrial stromal cells [5].   The glycogen metabolism of endometrial stromal cells is regulated by adiponectin through (a) increasing glucose transporter 1 expression, (b) inhibiting glucose catabolism via decrease in lactate and ATP production, (c) increasing glycogen synthesis, (d) promoting glycogen accumulation, and (e) enhancing glycogen secretion [5].  

            Other studies have found that adiponectin as a tissue regenerating hormone, whereby globular adiponectin increases proliferation, migration and myogenic properties of satellite cells and mesoangioblasts [3].  Adiponectin secretion from MAT has special significance because increases in adiponectin during caloric restriction has been found to be produced in MAT [6].  Ovarian dysfunction in mice has been discovered with adiponectin deficiency [7]. Evidence shows negative effects of adiponectin on GnRH secretion from the hypothalamus, LH and FSH secretion and testosterone in obese men [8].  

            Obesity and metabolic syndrome have been linked to hypothalamic-pituitary-adrenal axis dysfunction and local metabolism of glucocorticoids in adipose tissue [9].    In human and rat studies leptin inhibited ACTH-stimulated cortisol production but had no effect on basal cortisol production which effects metabolic pathways [9].  Leptin has a negative feedback loop on the hypothalamic-pituitary-adrenal axis, since leptin deficient mice have exhibited increased CRH, ACTH and adrenal cortex hormones.  Adiponectin receptors are present in human adrenals and that glucocorticoids and ACTH are known to decrease adiponectin production in WAT [9]. In adrenal neoplasia, local secretion of leptin and adiponectin was found [10]. This link with adrenal neoplasia, leptin and adiponectin can be used as a biomarker for adrenal neoplasia development.


1.   Menzbert, H., & Morrison, C. D. (2015). Structure, production and signaling of leptin. Metabolism, 64(1). 13-23. doi:10.1016/j.metabol.2014.09.010   

2.   Dos Santos, E., Pecquery, R., de Mazancourt, P., & Dieudonné, M. (2012). Adiponectin and Reproduction. Vitamins and Hormones, 90.187-209. http://dx.doi.org/10.1016/B978-0-12-398313-8.00008-7

3.   Fiaschi, T., Magherini, F., Gamberi, T., Modesti, P.A., & Modesti, A. (2014). Adiponectin as a tissue regenerating hormone: more than a metabolic function. Cellular and Molecular Life Sciences, 71. 1917-1925. doi:10.1007/s00018-013-1537-4

4.   Chehab, F.F. (2014). Leptin and reproduction: past milestones, present undertakings and future endeavors. Journal of Endocrinology, 223,(1). T37-T48. doi:10.1530/JOE-14-0413

5.   Duval, F., Dos Santos, E., Maury, B., Serazin, V., Fathallah, K., Vialard, F., & Dieudonné, M. (2018). Adiponectin regulates glycogen metabolism at the human fetal-maternal interface. Journal of Molecular Endocrinology, 61,(3). 139-152. https://doi.org/10.1530/JME-18-0013

6.   Cawthorn, W.P., Scheller, E.L., Learman, B.S., Parlee, S.D., Simon, B.R., Mori, H., … MacDougald, O.A. (2014). Bone marrow adipose tissue is an endocrine organ that contributes to increased circulating adiponectin during caloric restriction. Cell Metabolism, 20. 368-375. http://dx.doi.org/10/1016/j.cmet.2014.06.003

7.   Cheng, L., Shi, H., Jin, Y., Li, X., Pan, J., Lai, Y., … Li, F. (2016). Adiponectin deficiency leads to female subfertility and ovarian dysfunctions in mice. Endocrinology, 157(12). 4875-4887. doi:10.1210/en.2015-2080

8.   Martin, L.J. (2014). Implications of adiponectin in linking metabolism to testicular function. Endocrine, 46. 16-28. doi:10.1007/s12020-013-0102-0

9.   Kargi, A.Y., & Iacobellis, G. (2014). Adipose tissue and adrenal glands: novel pathophysiological mechanisms and clinical applications. International Journal of Endocrinology, 2014. 1-8. http://dx.doi.org/10.1155/2014/614074

10.  Letizia, C., Petramala, L., Rosaria, C., Di Gioia, T., Chiappetta, C., Zinnamosca, L, … Iacobellis, G. (2015). Leptin and adiponectin mRNA expression from the adipose tissue surrounding the adrenal neoplasia. The Journal of Clinical Endocrinology & Metabolism, 100(1). E101-E104. doi:10.1210/jc.2014-2274


4. Gastrointestinal and Neuropeptide Hormones


At the core of homeostasis is the balance between catabolism, anabolism and storage of biomolecules that must be replenished from outside nutrient sources. Thus, hormones involved in feeding behavior and gastrointestinal systems such as Neuropeptide Y (NPY), Ghrelin and Agouti-related peptide (AGRP) become important regulators in energy balance. As seen in the following diagram taken from González-Muniesa, Pedro, et al. 2017 article “Obesity”, these three hormonal signals feed and act in conjunction with one another to regulate nutrient uptake [1]. We will discuss them individually below:   

Ghrelin is a 28-amino acid peptide secreted from the gastrointestinal endocrine cells, concentrated in the stomach [2]. Ghrelin has been linked to the regulation of many homeostatic processes including heat production, insulin secretion, weight gain, and most famously, hunger. Ghrelin levels have found to be associated with food cues including visual, olfactory and ingestion signals [3]. One of the proposed mechanisms for Ghrelin signaling is as ligand to the Growth Hormone secretagogue (GHS) receptor, becoming a potent acute signal for Growth Hormone (GH) release [2]. In fact this mechanism not only allows Ghrelin regulation of food intake and the GI tract, but also an equally important role of glucose homeostasis and the pancreas. Studies have shown that pancreatic Ghrelin can rescue hypoglycemic states through GH release mediation; Ghrelin utilizes GH’s function to uptake energy and nutrients to process food from feeding [4]. Current research is interested in the mechanism of how Ghrelin influences the pancreas; some indicate a direct influence of Ghrelin on beta-cells while others indicate that ghrelin indirectly inhibits insulin through stimulation of somatostatin release [5].

While Ghrelin pathways are still unclear, many papers have indicated the involvement of NPY, a 36-residue long peptide that functions in multiple systems throughout the body including cardiovascular, gastrointestinal, neuroendocrine, and sympathetic systems. Research suggests that NPY is one of the downstream signals triggered by Ghrelin to carry out hunger activation. NPY is synthesized and released from both sympathetic neurons and the adrenal medulla. The biochemical explanation to NPY’s diverse functionality is its family of G-protein coupled receptors that enhance signaling via signal cascades starting with cAMP/PKA activation. These G-protein receptors are differentially localized throughout the body, allowing NPY to perform specified functionalities for each system. For example, the NPY Y5 receptor mainly effects feeding regulation whereas the NPY Y2 receptor has a broader range of effects including gastrointestinal motility and blood pressure regulation [6]. The research indicates that one pathway NPY regulates homeostasis is by driving energy conservation in Brown Adipose Tissue as well as peripheral mastication in motor systems; in effect, NPY tells our bodies to save nutrients while preparing to feed and replenish new nutrients. This pathway is driven through stimulation of NPY’s release in the paraventricular hypothalamic nucleus (PVH) and is inhibited by the sensation of food [7].

Another factor of Ghrelin’s hunger signaling is the Agouti-related protein (AGRP); at 131 amino acids long, AGRP is larger than Ghrelin and NPY and is expressed in the hypothalamic arcuate nucleus and adrenal medulla. AGRP is mechanistically antagonistic for melanocortin receptors MC3-R and MC4-R and all three molecules have been implicated in feeding [8]. In fact, AGRP is frequently mentioned in conjunction with NPY due to its exclusive co-expression in NPY arcuate nucleus neurons. Interestingly, the arcuate nucleus is also home to receptors for other metabolic factors such as insulin and growth hormone indicating a sort of homeostatic hub. Recent studies suggest that AGRP’s functionality is either separate or downstream of NPY’s pathway. Moreover, while AGRP has been found to influence resting metabolic rate and long-term appetite it does not seem to influence changes in fat as NPY does [9]. This indicates that while AGRP and NPY are both downstream of Ghrelin, the signaling pathway is not the amplification of one task, rather an incredibly diverse set of task combinations that could be independent of each other. 

Physiologically, this array of differentiated functions for hunger hormones and their receptors allow for different organs to perform specific tasks in the feeding, nutrient digestion and energy absorption process. Speculatively, this also allows for a safety net in case one regulatory hormone is not functional e.g.GH triggered by Ghrelin can rescue hypoglycemic states [4]. Finally, it also allows for nutrient processing to trigger other necessary systems e.g.NPY’s family of receptors allows simultaneous regulation of the GI tract and blood pressure, both important in the digestion and transport of nutrients [6].


1.   González-Muniesa, Pedro, et al. (2017, June 15). Figure 6: Control of hunger and satiety. [Digital image]. Retrieved from https://www.nature.com/articles/nrdp201734#f6

2.   Kojima M., Hosoda H., Matsuo H. and Kangawa K. (2001) Ghrelin: discovery of the natural endogenous ligand for the growth hormone secretagogue receptor. Trends Endocrinol. Metab. 12, 118–122 10.1016/S1043-2760(00)00362-3

3.   Malik S., McGlone F., Bedrossian D. and Dagher A. (2008) Ghrelin modulates brain activity in areas that control appetitive behavior. Cell Metab. 7, 400–409 10.1016/j.cmet.2008.03.007

4.  Zhao T‐J, Liang G, Li RL, et al. Ghrelin O‐acyltransferase (GOAT) is essential for growth hormone‐mediated survival of calorie‐restricted mice. Proc Natl Acad Sci U S A. 2010;107(16):7467‐7472.

5.   DiGruccio MR, Mawla AM, Donaldson CJ, et al. Comprehensive alpha, beta and delta cell transcriptomes reveal that ghrelin selectively activates delta cells and promotes somatostatin release from pancreatic islets. Mol Metab. 2016;5(7):449‐458.

6.   Li, L., Najafi, A. H., Kitlinska, J. B., Neville, R., Laredo, J., Epstein, S. E., et al. (2011). Of mice and men: neuropeptide Y and its receptors are associated with atherosclerotic lesion burden and vulnerability. J. Cardiovasc. Transl. Res.4, 351–362. doi: 10.1007/s12265-011-9271-5

7.   Nakamura, Y., Yanagawa, Y., Morrison, S. F., & Nakamura, K. (2017). Medullary Reticular Neurons Mediate Neuropeptide Y-Induced Metabolic Inhibition and Mastication. Cell Metabolism,322-334. doi:10.1016/j.cmet.2016.12.002

8.   Fan, W., Boston, B. A., Kesterson, R. A., Hruby, V. J., & Cone, R. D. (1997). Role of melanocortinergic neurons in feeding and the agouti obesity syndrome. Nature,165-168. doi:10.1038/385165a0

9.   Broberger, Christian et al. “The Neuropeptide Y/agouti Gene-Related Protein (AGRP) Brain Circuitry in Normal, Anorectic, and Monosodium Glutamate-Treated Mice.” Proceedings of the National Academy of Sciences of the United States of America 95.25 (1998): 15043–15048. Print.


5. Exercise


Exercise produces a number of beneficial health factors and is associated with reduced risk for disease, such as type 2 diabetes, coronary heart disease, and stroke [2,3]. The effects of exercise are accomplished through modulation of endocrine physiology. The stress placed on the body during exercise produces an abrupt shift from homeostasis [5]. This shift causes a number physiological changes including: increased growth hormone, increased cortisol secretion, increased levels of testosterone, and increased cytokine release allowing the body to adapt to the increasing metabolic demands [5,9].

According to the exploratory HERM model (Hormonal Exercise Response Model), the physiological changes brought about by exercise occur in three phases starting with immediate neural signaling from the sympathetic nervous system [5]. Signaling from the sympathetic nervous system activates the adrenal medulla, which secretes catecholamines (i.e. epinephrine and norepinephrine). The increased circulation of epinephrine and/or the sympathetic neural signaling inhibits the secretion of insulin, which causes the body to adapt in order to provide enough glucose to for cells to properly function [10]. Although the mechanism has yet to be completely defined, studies suggest that the body adapts by synthesizing more glucose transporters as well as directing other intracellular glucose transporters to the plasma membrane thus allowing more glucose to enter the cell with decreased levels of insulin [10].

            In the second phase of hormonal response to exercise, the hypothalamus releases a number of hormones including: corticotropin releasing hormone (CRH), growth hormone releasing hormone (GHRH), and gonadotropin releasing hormone (GnRH) [5]. CRH signals the pituitary to release ACTH, which is then carried through the bloodstream to the adrenal cortex where it stimulates the secretion of cortisol. Increased cortisol produces the net result of increased plasma concentrations of amino acids, glucose, and free fatty acids, which are all necessary for sufficient nutrients during bouts of exercise. Similarly, the secretion of GHRH from the hypothalamus stimulates the pituitary to secrete growth hormone. Increased growth hormones primary effects are: stimulating growth and increased protein synthesis and the secondary effects are aimed at carbohydrate and lipid metabolism similar to the effects of cortisol [5,10]. Specifically, a greater concentration of growth hormone increases gluconeogenesis and lipolysis while inhibiting insulin to produce increased plasma concentrations of glucose necessary during strenuous exercise [10]. Finally, increased levels of GnRH secreted by the hypothalamus signals the pituitary to secrete FSH and LH. In men, LH stimulates the testes to make testosterone. Therefore, in response to exercise men produce increased testosterone mainly through the hypothalamic-pituitary-testes pathway [9]. However, to a lesser degree, the release of testosterone during exercise may be achieved through other mechanisms [9]. For example, spillover from the activation of the adrenal cortex (e.g. for secretion of cortisol) also leads to the secretion of sex hormones [9]. Moreover, testosterone is also produced in small amounts in the ovaries of females. Spillover effects and the conversion of testosterone to estradiol in the ovaries are the primary mechanisms for increasing testosterone in women and in prepubescent males- this explains why women and prepubescent males do not show a large increase in testosterone in response to exercise [9]. In summary, there is an increase in growth hormone, cortisol, and testosterone in response to exercise that are all produced through endocrine responses to exercise.

            The third phase of the hormonal response to exercise is marked by the modulating influence of circulating hormones [5]. Feedback systems begin modulating hormone levels and controlling for the amount of energy necessary to maintain function during ongoing exercise. Importantly, during this phase contracting skeletal muscles release myokines (i.e. a subset of cytokines) which cause autocrine, endocrine, and paracrine responses in target tissues [5,10]. A specific myokine, IL-6, is primarily involved in energy mobilization providing fuel for local and systemic use. Studies have also found that IL-6 is involved in other metabolic processes such as regulating muscle stem cell- mediated hypertrophy [7]. The activity of myokines, in response to muscle contraction, provides evidence for muscle maintaining a major endocrine role and a modulator of energy mobilization.

            Exercise also maintains influence on energy and metabolism through appetite. Research suggests that exercise has a negative effect on appetite by inhibiting the hormone ghrelin- a hormone that stimulates hunger [1,8]. Moreover, studies have shown that exercise increases sensitivity to insulin thus requiring less concentration of insulin to transport glucose into cells for energy [4]. These findings suggest that exercise must maintain a modulatory role on metabolism and appetite. However, research from Pontzer (2017) has questioned the effect of exercise on metabolic rate [6]. His research has shown that active individuals burn the same amount of calories per day as individuals who live less active lives [6]. This appears counterintuitive, as it would seem those who are more active burn more calories but as Pontzer (2017) goes on to explain: metabolism in humans has been maximized by evolution [6]. Compared to our ape ancestors, humans have evolved to possess a higher metabolism that affords advantages such as allocating enough nutrients to feed our larger brains [6]. Pontzer (2017) argues convincingly that exercise doesn’t change a person’s metabolic rate, rather exercise causes adaptation of metabolic mechanisms to operate more efficiently (e.g. increased insulin sensitivity) and that weight control largely depends on an individual’s diet as a person’s metabolic rate is, more or less, fixed [6].


1.  Broom, D. R., Batterham, R. L., King, J. A., & Stensel, D. J. (2009). Influence of resistance and aerobic exercise on hunger, circulating levels of acylated ghrelin, and peptide YY in healthy males. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology,296(1). doi:10.1152/ajpregu.90706.2008

2.  Fentem, P. H. (1994). ABC of Sports Medicine: Benefits of exercise in health and disease. Bmj,308(6939), 1291-1295. doi:10.1136/bmj.308.6939.1291

3.  Garber, C. E., Blissmer, B., Deschenes, M. R., Franklin, B. A., Lamonte, M. J., Lee, I., . . . Swain, D. P. (2011). Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults. Medicine & Science in Sports & Exercise,43(7), 1334-1359. doi:10.1249/mss.0b013e318213fefb

4.  Goodyear, P. L., & Kahn, M. B. (1998). Exercise, Glucose Transport, And Insulin Sensitivity. Annual Review of Medicine,49(1), 235-261. doi:10.1146/annurev.med.49.1.235

5.  Hackney, A. C., & Lane, A. R. (2015). Exercise and the Regulation of Endocrine Hormones. Progress in Molecular Biology and Translational Science Molecular and Cellular Regulation of Adaptation to Exercise,293-311. doi:10.1016/bs.pmbts.2015.07.001

6.  Pontzer, H. (2017). The Exercise Paradox. Scientific American,316(2), 26-31. doi:10.1038/scientificamerican0217-26

7.  Serrano, A. L., Baeza-Raja, B., Perdiguero, E., Jardí, M., & Muñoz-Cánoves, P. (2008). Interleukin-6 Is an Essential Regulator of Satellite Cell-Mediated Skeletal Muscle Hypertrophy. Cell Metabolism,7(1), 33-44. doi:10.1016/j.cmet.2007.11.011

8.  Vatansever-Ozen, S., Tiryaki-Sonmez, G., Bugdayci, G., & Ozen, G. (2011). The Effects of Exercise on Food Intake and Hunger: Relationship with Acylated Ghrelin and Leptin. Journal of Sports Science & Medicine10(2), 283–291.

9.  Vingren, J. L., Kraemer, W. J., Ratamess, N. A., Anderson, J. M., Volek, J. S., & Maresh, C. M. (2010). Testosterone Physiology in Resistance Exercise and Training. Sports Medicine,40(12), 1037-1053. doi:10.2165/11536910-000000000-00000

10.  Widmaier, E. P., Raff, H., & Strang, K. T. (2016). Vanders human physiology: The mechanisms of body function(14th ed.). New York, NY: McGraw-Hill Education.


6. Conclusion


In this paper, we reviewed just a subset of the complex endocrine system and its vital role in homeostasis: First, Insulin is an important hormone that regulates glucose levels in the bloodstream through the pancreatic beta-cells secretion.  Four different pathways were discussed on how insulin secretion is stimulated.  Insulin antagonists such as epinephrine, cortisol and growth hormones were also discussed in relation to hypoglycemia and stress.  Second, Adipose tissue secretes leptin and adiponectin via adipocytes to help control energy balance, regulate glucose levels and fatty acid breakdown.  Leptin and adiponectin affects the reproductive axis as well, whereby Leptin has roles in puberty and pregnancy.  Third, Ghrelin, NPY and AGRP are important regulatory hormones linked to: (a) GI metabolism, (b) insulin secretion, (b) blood pressure, and (d) hunger triggering.  Finally, exercise has complex endocrine effects; we discuss the HERM model, e.g. exercise induced physiological changes that occur in three phases.  We suggest that exercise can inhibit the ghrelin hormone, thus having negative effects on appetite.

            Through this paper we see that human behaviors such as exercise, organs such as adipose, and hormones such as Ghrelin collaborate to maintain homeostasis. For example, exercise heightens efficiency of food metabolism whose intake is triggered by Ghrelin. These nutrients are processed in pathways involving NPY, AGRP, and Insulin signaling to be stored in Adipose tissues that signals a well-nourished body suitable for development or pregnancy. Speculatively, this correlates homeostasis with higher evolutionary fitness. Our review shows that future research directions revolve around treatments of obesity and diabetes. For example, Insulin is now a key indicator in clinical research on effects of exercise and hormones on obesity reversal i.e providing GH or ghrelin to balance serum insulin/sugar levels. As our understanding of the endocrine system increases, we expect future research to continue development of new generation therapy and to test efficacy of treatment in expanded diabetic patient populations.


Advantages and Limitation of GWAS by Dr. Paul Cottrell


With the human genome being sequenced we can perform studies on single nucleotide polymorphisms (SNP) of the whole genome. This sort of study is called genome-wide association studies (GWAS). The goal with GWAS is to make associations of SNPs and diseases. Unfortunately, there are challenges to accurately make these associations between SNPs and diseases. Some have proposed that current biostatistical analysis paradigms need to be more holistic to recognize the true complexity of the genotype-phenotype relationship (Moore, Asselbergs and Williams, 2010). In this essay I go over some of the advantages and limitations of GWAS pertaining to sample size, variant frequency and data interpretation.


There is a utility with GWAS in finding genotype-phenotype relationships that are associated with common and complex diseases (Ghazani, 2017). For diseases that are common it is relatively easy to meet the large sample size requirement of GWAS. The cost of processing GWAS is reasonable, due to the low cost of sequencing and the ability to data mine large data sets. GWAS requires the bi-allelic assumption, which is reasonable because only 1-3% of our genome has random copy number variants. Many studies have been performed and data banked on different diseases, but researchers need to be aware of the methodology used to determine if the genotype-phenotype association conclusions are valid. If the controls and cases are of similar distributions then statistical significance is much stronger, which is very important to consider when validating other researchers conclusions of genotype-phenotype association.


GWAS under certain conditions fails to identify new susceptibility loci for some diseases, even though a study might have a very large sample size (Moore, Asselbergs, and Williams, 2010). GWAS performs poorly in determining genotype-phenotype association with rare diseases because it is difficult to obtain the required large sample size. Validation and discovery sample overlap is a potential pitfall of GWAS. The validation sample is an independent sample with known phenotypes, whereas the discovery sample is where SNPs are selected and estimations of their effects determined. Another pitfall with GWAS pertains to the validation sample, whereby prediction accuracy will be overestimated if the validation sample is closely related to the discovery sample than the target sample (Wray et al., (2013). Lastly, Wray et al. (2013) proposed that population stratification similarity can inflate accuracy when discovery and validation sample stratification matches population stratification, but do not match the targeted sample stratification. Researchers should pay close attention to this stratification issue to assure validity in genotype-phenotype association conclusions. Another important limitation of GWAS is the use of standard logistical regression. Moore et al. (2010) suggested the use of more advanced algorithms: data mining with machine learning, use of decision trees and random forests. These more advanced algorithms for the use in GWAS is to help illuminate the relationship with DNA sequences variation, environmental exposure and variation in disease susceptibility.


GWAS is a powerful method to determine genotype-phenotype association, but researchers must keep in mind the limitation of sample size, variation frequency, and specific data interpretations to come up with valid conclusions of association.


Ghazani, A. A. (2017). Introduction to Genomics [PowerPoint slides]. https://canvas.harvard.edu/courses/35084/files/5290181?module_item_id=354933

Moore, J. H., Asselbergs, F. W., & Williams, S. M. (2010). Bioinformatics challenges for genome-wide association studies. Bioinformatics, 26(4), 445-455. doi:10.1093/bioinformatics/btp713

Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., & Visscher, P. M. (2013). Pitfalls of predicting complex traits from SNPs. Nature Reviews Genetics, 14, 507-515. doi:10.1038/nrg3457

Potential Benefits and Drawbacks to Direct-to-Consumer Genetic Testing by Dr. Paul Cottrell


With the advent of the genomic age we are now able to sequence individual genomes and determine the likelihood of developing a disease state. Due to the lower cost of sequencing DNA at a large scale there has been a plethora of DNA kits for the consumer market. This opens opportunities for direct contact to the consumer about their individual ancestry and potential diseases. These opportunities on the surface seem useful for the consumer, but we must consider the validity of the pathological or non-pathological claims. This direct-to-consumer genetic testing market might need to have better regulatory oversight to maintain validity and quality of the genetic claims. In addition to collecting DNA information on individuals, it is possible to correlate genetic linkage maps on larger population sets to see patterns of disease and potential therapies. With any personal information security is of the upmost importance, especially with individual DNA sequences. 

Potential Benefits 

The purpose of personal genetic testing is to determine the following: risk of disease, screening newborns, directing clinical management, identifying carriers, and establishing prenatal or clinical diagnosis or prognosis (Shuren, 2010). With proper oversight from the Food and Drug Administration (FDA) consumers can have a higher assurance of the validity of the DNA testing kits used for their individual genetic testing. Instead of visiting a health care provider for genetic screening, consumers can obtain their genetic profiles without over burdening the current health care system. This method of distribution to obtaining DNA material is a lower cost alternative. In terms of informing consumers about their genetic profile, there is the potential in catching diseases early and mitigating some, if not all, the effects of the disease. This has a huge psychological effect on the consumer, especially with family history of a potentially serious disease, e.g. Huntington’s Disease or Alzheimer’s Disease. In terms of prenatal testing, parents can prepare for potential serious childhood diseases and decide if the pregnancy should be terminated. Even though prenatal testing involves a health care provider to perform, it can be compared to DNA screening for commercial kits of the parents. In terms of family history of cancer, direct marketing to consumers for DNA screening has the potential for disease prevention and early detection (Kontos and Viswanath, 2011). By searching for oncogenes and potential genomic structural issues that might promote the pathology of cancer through DNA processing companies, consumers can get probability distribution curves. This allows for consumers be actively involved in their health care management, which has positive externalities from a psychological perspective. 

Potential Drawbacks

There are many potential drawbacks to direct consumer genetic testing: testing individuals that are not at high risk, security issues with storing of generic material, over burden of sequencing a mass population, validity of genetic profile claims, negative psychological effects of knowing the potentiality of a deadly disease without any clinical signs, income disparity issues, and educational hurtles in the pubic with understanding medical and statistical concepts. Professional health care workers are concerned with the over testing of low risk populations, as well as the lack of linkage of genetic variants and pathological disease states (Kontos and Viswanath, 2011). Higher literacy in understanding the genetic profile results are needed amongst the public to fully utilize their profile information. It is possible that an ill-informed public on genetic profiles might increase clinical visits, whereby increasing health care costs. Another potential negative side effect is the erosion of the patient-provider relationship (Kontos and Viswanath, 2011). The American Society of Human Genomics recommended the following to improve direct-to-consumer genetic testing: transparency of information provided to the consumer, inform the public that certain genetic tests might lack analytic and clinical validity, ensure DNA testing and laboratory quality (Hudson et al., 2007). 


The genomic age has provided researchers, health care providers, and patients with some new tools to help manage and surveil serious disease states. This new age of patient involved medicine can be a powerful tool if the regulatory framework is in place to ensure quality of genetic profiles. In addition to better regulatory frameworks for direct-to-consumer genetic testing, consumer involvement and medical education are needed to truly discern what their genetic profile means in terms of potential pathologies. 

References Hudson, K., Javitt, G., Burke, W., & Byers P. (2007). ASHG statement on direct-to-customer genetic testing in the United States. The American Journal of Human Genetic 81, 635- 637. Retrieved from www.ajhg.org.

Kontos, W. Z., & Viswanath, K. (2011). Cancer-related direct-to-consumer advertising: a critical review. Nature Reviews, 11, 142-150. Retrieved from www.nature.com/reviews/cancer. 

Shuren, J. (2010). Direct-to-consumer genetic testing and the consequences to the public. The Subcommittee on Oversight and Investigations. Retrieved from http://www.fda.gov/NewsEvents/Testimony/ucm219925.htm. 

The Privacy Paradox in Genomics by Dr. Paul Cottrell


With the advent of information technology, we are now able to collect and analyze large sums of data. It is not uncommon now to hear in the mass media terms such as artificial intelligence, business analytic, data mining, neural networks and the like. This is somewhat of a new phenomenon because of the cost of computing is reasonable for performing complex calculations and the storage of large sums of data. In the medical industry informational technology is deployed in many ways, e.g. medical imaging, patient record keeping and biochemistry research. There are some that propose that this informational technology boom will be utopic and societies will be more efficient—there is a downside that is quite dystopic. This downside is an erosion of privacy and security of such personal medical information. In this essay the I will explore the privacy paradox and the positive and negative effects of this informational technology boom in the medical industry. 

Privacy Paradox 

Personal information is usually considered private information and not for public domain consumption. Many countries have laws in protecting citizens from ill use of private information, especially in the context of medical information (Büschel et al., 2013). The privacy paradox is defined as the contradiction between individuals’ motivation in allowing the usage and processing of personal information and the fear of security breaches of this information to an unauthorized person or institutions (Büschel et al., 2013). A perfect example of this is when Ellen Richardson was denied entry in to the United States of America due to a history of clinical depression (Hauch, 2013). How and why should a foreign entity have access to medical records of foreign passengers? In this case the usage of Ms. Richardson’s information was for national security reasons, albeit a bogus reason. This leads us to the positive and negative effects of the power of informational technology. 

Positive and Negative Effects 

There are positive effects with the use of information technology with personal medical data. For example, if there was a centralized database of all the medication that patients are on then an artificial intelligent system can flag dangerous medication combinations. Another possible positive effect with the use of personal medical data is in the realm of genetics. If every patient has their genome sequenced, then physicians would be able to leverage this genomic information and provide customized care and provide better surveillance on potential diseases that have not shown clinical signs yet. With the implementation of data mining, genomic data of societies can provide novel insights of diseases within subpopulations, which would potentially help in providing customized care for patients. The negative effects of increase collection of personal medical records are many. For example, hacking of personal information is a real risk. Personal identities can be stolen and to reclaim these identities may take years and involve legal actions involving large monetary costs (Taitsman et al., 2013). Another important negative effect is the potential scope creep of collecting personal data. There seems to be a trend in the United States of America that both social media companies and government agencies are trying to collect every possible digital footprint of a person. These social media companies are using this data to sell to advertisers or private corporations for profit motives. As for government agencies, they use this digital footprint for national security and tend to curtain the civil liberties of a citizen. For example, if there where persons that where predisposed as a carrier of a certain deadly disease that government might curtail the movement of such citizens, e.g. HIV positive persons. 


In the final analysis, it is important to question data collection and understand how personal information is protected and how to create boundaries, whereby this data is not used to the detriment of the citizen. 


Büschel, I., Mehdi, R., Cammilleri, A., Marzouki, Y., & Elger, B. (2014). Protecting human health and security in digital Europe: How to deal with the “Privacy Paradox”? Science and Engineering Ethics, 20(3), 639-658. doi: 10.1007/s11948-013-9511-y 

Hauch, V. (2013, November 28). Disabled woman denied entry to U.S. after agent cites supposedly private medical details. Toronto Star. Retrieved from https://www.thestar.com/news/gta/2013/11/28/disabled_woman_denied_entry_to_us_afte r_agent_cites_supposedly_private_medical_details.html 

Taitsman, J. K., Grimm, C. M., & Agrawal, S. (2013). Protecting patient privacy and data security. The New England Journal of Medicine, 368(11), 977-979. Retrieved from www.nejm.org. 

Genomic Influences on Behavior by Dr. Paul Cottrell


There have been many investigations on the linkage between genetics and aggression. Some work has been conducted with the monoamine oxidase A gene (MAOA) and behavioral aggression (McDermott et al., 2009). With many genetic experiments Drosophila melanogaster has been a reliable model in gaining understanding on genotype and phenotype correlations. It has been shown that the fru gene is critically involved in sex-specific patterns of aggression and forming dominance (Vrontou et al., 2006). Through fMRI studies of genetic variants, the serotonin pathway many be useful in understanding the coupling of genetics, brain physiology and aggressive behavior (Craig and Halton, 2009). Below will go into a little more detail of genomic influence on human behavior and the social implications. 

Genomic Influences on Human Behavior 

Human behavior is a complex system that is influenced by genomic, brain physiology and environmental cues. Due to this complexity it is hard to narrow the causal relationship between specific genes and human behavior. The hypothalamus, pituitary and adrenal axis affect stress levels. The serotonin pathway has been involved in aggressive behavior, as well as hypoglycemia in certain individuals (Craig and Halton, 2009). The evidence of specific genes that impact aggression is mixed. Androgen receptors associated with shorted CAG repeats seem to imply increased aggression in some populations (Rajender et al., 2008). MAOA gene has been linked to the aggressive behavior through oxidizing of serotonin, norepinephrine and epinephrine. When MAOA gene has variable number tandem repeats there seems to be an association to increased aggression (Craig and Halton, 2009). 

Social Implications 

There are several social implications of linking genetics and aggressive behavior. Firstly, it is the age-old debate on nature vs. nurture. Scientists tend to discredit the potential positive and negative feedback loops of socialization and how this affects aggressive behavior. Even though genetics have a major role to play in behavioral wiring, so does the socialization experience (Sapolsky, 2017). This observation from Sapolsky might help explain the mixed results in genetic research involving human behavior. Secondly, if there can be a reliable linkage between genetic defects and abnormal behavior in humans there might be social stereotyping of these affected individuals, which might produce a self-fulling prophecy. These affected individuals might feel they have no free will or control of their own destiny. This technocracy in the realm of genetics might lead to individuals guilty of a crime before a crime is committed. Thirdly, if genetic behavioral disease is predicted it might prevent employment in certain professions for these individuals, whereby further social stereotyping will result. Insurance companies might even raise rates for such individuals with genetic abnormalities, even though without clinical presentation. Another perspective is that society might force prophylactic psychiatric treatment without the consent of the patient. This is especially troubling in a free society with patients that are not showing clinical signs. The main point here is that just because there are linkages between genetic abnormalities and negative human behavior does not mean that there are no complex pathways which might mitigate the negative behavior. 


Craig, I. W., & Halton, K. E. (2009). Genetics of human aggressive behavior. Human Genetics, 126, 101-113. doi: 10.1007/s00439-009-0695-9 

McDermott, R., Tingley, D., Cowden, J., Frazzetto, G., & Johnson, D. D. P. (2009). Monoamine oxidase A gene (MAOA) predicts behavioral aggression following provocation. Proceedings of the National Academy of Sciences of the United States of America, 106(7), 2118-2123. http://www.pnas.org/cgi/doi/10.1073/pnas.0808376106 

Rajender, S., Pand, G., Sharma, J. D., Gandhi, K. P. C., Singh, L., & Thangaraj, K. (2008). Reduced CAG repeats length in androgen receptor gene is associated with violent criminal behavior. International Journal of Legal Medicine, 122, 367-372. 

Sapolsky, R. M. (2017). Behave: The biology of humans at our best and worst. New York, NY: Penguin Press. 

Vrontou, E., Nilsen, S. P., Demir, E., Kravitz, E. A., & Dickson, B. J. (2006). Fruitless regulates aggression and dominance in Drosophila. Nature Neuroscience, 9(12), 1469-1471. doi:10.1038/nn1809

Advantages and Drawbacks of Next Generation Sequencing by Dr. Paul Cottrell


Starting in 1990 the Human Genome Project (HGP) allowed the sequencing of the whole human genome by 2003 (Gibson and Muse, 2009). The Sanger sequencing technique is a gold standard for sequencing DNA and was instrumental for HGP (Gibson and Muse, 2009). Unfortunately, Sanger sequencing involves much time and money to perform, and throughout the decades other methods of sequencing have been developed to reduce time and cost for sequencing large scale projects. In addition to time and cost of sequencing, hurdles have arisen—such as correct sequence assembly of repeated regions of DNA and how to navigate through mega-datasets. In this essay throughput, cost and other technological and biological aspects will be summarized for the next generation of sequencing technologies.

Throughput and Cost

There are three generations of sequencing techniques: the first, second and third generation. The first-generation is the Sanger sequencing method which used ddNTPs as fragment terminators to allow labels fragments to be separated via gel electrophoresis. Then dye-terminating coupled with capillary electrophoresis allowed for a higher throughput from the standard 1977 sanger sequencing technique (Pavopoulos et al., 2013). The HGP costed about $3 billion using this type of method. The second-generation sequencing allows up to millions of short sequences at high throughput, which allows for clinical applications because of the read depth is much more then the previous generation of sequencing techniques. Second-generation sequencing can have 30x of coverage or more and is highly used today to preform complex sequencing investigations (Pavopoulos et al., 2013). The cost to sequence whole genomes with this technology have drastically reduced from millions of dollars to thousands of dollars. Third generation or next generation of sequencing, would allow for whole human genome sequencing in a matter of hours at low cost. Some of the players in this space are Helicos BioSciences, Pacific Biosciences, Oxford Nanopore and Complete Genomics (Pavopoulos et al., 2013). The cost to sequence whole genomes is estimated to be within $1,000 or less. Even though the next generation of sequencing reduces time and cost of sequencing data, clinical applications do tend to be confirmed using Sanger sequencing methods due to these next generation methods having issues with copy number variants (Ghazani, 2017) (Zhao et al., 2013). These next generation systems leverage advances from second generation systems in DNA-seq for determining unknown genome or variation analysis, RNA-seq for analyzing gene expression, or Chip-seq for protein binding sites on DNA (Pavlopoulos et al., 2013). Faster and cheaper sequencing may lead to less accurate sequencing due to copy number variations because the inherent statistical methods used for assembly.

Other Technological and Biological Aspects

As for the technological aspects of next generation sequencing methods the following are important to consider: file formats, alignment tools, genomic browsers and visualization methods for comparative genomics. With the expansion of sequencing data and different software companies developing methods to utilize these datasets, there needs to be a standardization for archiving genomic data. For example, FASTQ, SAM/BAM, or VCF are standards for holding gigabytes or terabytes of data. Pavlopoulos et al. (2013) have shown that at least twelve different software can be used for predicting structural variations in sequences, whereby some software uses proprietary input files. Some software for predicting structural variations are better at a combination of single-end, genome referencing, insertion detection and deletion detection; whereas other software performs better with pair-end, translocation across chromosomes and within chromosomes (Pavlopoulos et al., 2013). With all these different alignment tools researchers can become bewildered in navigating through each of the specific uses for structural variation prediction. Genomic browsers display the sequencing and annotation within a graphical user interface. Most of these genomic browsers have common features that help the user’s experience, but navigating through large sequences remains a challenge even though the search algorithms within these genomic browsers are becoming more efficient. This leads us to consider the need to visualize the genome sequence better. Not just visualizing linear data sequences but the 3-dimensional representation of the DNA itself. Virtual Reality (VR) and Augmented Reality (AR) might be a possible tool to help in navigating through large datasets and producing flythrough options for the researcher to see the sequence structure in 3-dimensions. These VR and AR systems have been used in biological research, such as protein shape; but these systems are far from being commercially viable. In the interim to VR and AR systems, focus has been on the algorithms that deal with alignment of unfinished genomes, intra/inter chromosome rearrangement and functional element identification for comparative genomics (Pavlopoulos et al., 2013).


Ghazani, A. A. (2017). Introduction to Genomics [PowerPoint slides]. Retrieved from https://canvas.harvard.edu/courses/35084/modules

Gibson, G., & Muse, S. V. (2009). A Primer of Genome Science (3rd ed.). Sunderland, MA: Sinauer Associates Inc.

Pavlopoulos, G. A., Oulas, A., Iacucci, E., Sifrim, A., Moreau, Y., Schneider, R., … Iliopoulos, I. (2013). Unraveling genomic variation from next generation sequencing data. BioData Mining, 6(13). http://www.biodatamining.org/content/6/1/13.

Zhao, M., Wang, Q., Wang Q., Jia P., & Zhao Z. (2013). Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspective. BMC Bioinformatics, 14(11). http://www.biomedcentral.com/1471-2105/14/S11/S1.