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University students often face high levels of stress and sleep disturbances due to their academic demands and lifestyle factors(1). Ashwagandha (Withania somnifera), an adaptogenic herb, has shown the potential to mitigate stress and improve cognitive function(2). However, limited research has examined its effects on these variables in university students. This study aimed to determine the effects of ashwagandha supplementation on sleep quality, mood, and cognitive function in university students.
A randomized, double-blind, placebo-controlled crossover study was used. Nine university students (5 males, 4 females; Age: 21±1 years; BMI: 25±2.5 kg/m2) were randomly assigned to receive 500 mg of standardized ashwagandha root extract capsules for 7 days or a placebo (encapsulated cornstarch) with a 7-day washout between treatments. Sleep was measured during the 7-day supplementation period using the Loughborough Daily Sleep Diary. Postsupplementation mood and cognitive function were measured by the Profile of Mood States (POMS) scale(3) and computerised Stroop, and Deary-Liewald simple and choice reaction tasks(4). Paired sample t-tests were used to determine differences between the ashwagandha and placebo conditions with calculated effect sizes (Cohen’s d).
Participants reported lower confusion indicator on the POMS following ashwagandha compared to the placebo (mean±SD: 4.8±2.0 vs 7.6±3.1 arbitrary units; P=0.03; d = −0.92). No other differences were found for any other mood indicators, sleep, or cognitive function parameters (P > 0.05).
These data suggest that ashwagandha may improve feelings of confusion in university students but further studies with larger sample sizes are needed to verify these findings and elucidate the underlying mechanisms.
Weight loss results in obligatory reductions in energy expenditure (EE) due to loss of metabolically active fat-free mass (FFM). This is accompanied by adaptive reductions (i.e. adaptive thermogenesis) designed to restore energy balance while in an energy crisis. While the ‘3500-kcal rule’ is used to advise weight loss in clinical practice, the assumption that EE remains constant during energy restriction results in a large overestimation of weight loss. Thus, this work proposes a novel method of weight-loss prediction to more accurately account for the dynamic trajectory of EE. A mathematical model of weight loss was developed using ordinary differential equations relying on simple self-reported inputs of weight and energy intake to predict weight loss over a specified time. The model subdivides total daily EE into resting EE, physical activity EE, and diet-induced thermogenesis, modelling obligatory and adaptive changes in each compartment independently. The proposed model was tested and refined using commercial weight-loss data from participants enrolled on a very low-energy total-diet replacement programme (LighterLife UK, Essex). Mathematical modelling predicted post-intervention weight loss within 0.75% (1.07 kg) of that observed in females with overweight or obesity. Short-term weight loss was consistently underestimated, likely due to considerable FFM reductions reported on the onset of weight loss. The best model agreement was observed from 6 to 9 weeks where the predicted end-weight was within 0.35 kg of that observed. The proposed mathematical model simulated rapid weight loss with reasonable accuracy. Incorporated terms for energy partitioning and adaptive thermogenesis allow us to easily account for dynamic changes in EE, supporting the potential use of such a model in clinical practice.
Minerals are supplemented routinely to dairy cows during the dry period to prevent metabolic issues postpartum. However, limited information exists on the impacts of mineral supplementation on colostrum carotenoids. This study aimed to determine the effects of prepartum supplementation with three micro-nutrients; inorganic selenium (INORG), organic selenium (ORG) or rumen-protected choline (RPC) on the carotenoid content of bovine colostrum and transition milk (TM) from pasture-based dairy cows. A total of 57 (12 primiparous and 45 multiparous) Holstein-Friesian (HF) and HF × Jersey (JEX) cows were supplemented daily for 49 ± 12.9 d before calving. Colostrum samples were collected from all cows immediately postpartum and TM one to five (TM1–TM5) were collected from a sub-set of 15 cows (five per treatment group) at each consecutive milking postpartum. Carotenoid concentration was determined using ultra-high performance liquid chromatography – diode array detection (UHPLC-DAD). With the use of transmittance, the colour index and colour parameters a*, b* and L* were used to determine colour variations over this period. Prepartum supplementation did not have a significant effect on colostrum β-carotene concentration or colour. Positive correlations between β-carotene and colour parameter b* (R2 = 0.671; P < 0.001) and β-carotene and colour index (R2 = 0.560; P < 0.001) were observed. Concentrations of β-carotene were highest in colostrum (1.34 μg/g) and decreased significantly with each milking postpartum (TM5 0.31 μg/g). Breed had a significant effect on colostrum colour with JEX animals producing a greater b* colostrum than HF animals (P = 0.030). Primiparous animals produced colostrum with the weakest colour compared to second or ≥third parity animals (P = 0.042). Despite statistical increases in the b* parameter in colostrum from JEX cows and multiparous cows, β-carotene concentrations did not significantly increase suggesting that other factors may influence colostrum colour. The b* parameter may be used as an indicator for estimating carotenoid concentrations in colostrum and TM, particularly when assessed via transmittance spectroscopy.
In pasture-based dairy production systems, identifying the appropriate stocking rate (SR; cows/ha) based on the farm grass growth is a key strategic decision for driving the overall farm business. This paper investigates a number of scenarios examining the effects of SR (2–3 cows/ha (0.25 unit changes)), annual nitrogen (N) fertilizer application rates (0–300 kg N/ha (50 kg/ha unit changes)), soil type (heavy and a free-draining soil) and agroclimate location ((south and northeast of Ireland) across 16 years) on pasture growth and forage self-sufficiency using the pasture-based herd dynamic milk model merged with the Moorepark St Gilles grass growth model. The modelled outputs were grass growth, grass dry matter intake, silage harvested and offered, overall farm forage self-sufficiency and N surplus. The model outputs calculated that annual grass yield increased from 9436 kg DM/ha/year when 0 kg N/ha/year was applied to 14 996 kg DM/ha/year when 300 kg N/ha/year were applied, with an average N response of 18.4 kg DM/kg N applied (range of 9.9–27.7 kg DM/kg N applied). Systems stocked at 2.5 cows/ha and applying 250–300 kg N fertilizer/ha/year were self-sufficient for forage. As N input was reduced from 250 kg N/ha/year, farm forage self-sufficiency declined, as did farm N surplus. The results showed that a reduction in N fertilizer application of 50 kg/ha/year will require a reduction in an SR of 0.18 cows/ha to maintain self-sufficiency (R2 = 0.90).
Little information is available on the phenotypic performance of perennial ryegrass varieties when exposed to grazing conditions on commercial grassland farms. Grass varieties are classically evaluated in mechanically defoliated plot systems which, although designed to mimic grazing conditions, do not fully capture the range of stresses or interactions that a sward is subjected to under commercial settings or over any period longer than 4 years. The evolution of technology in the form of PastureBase Ireland has led to agronomic data of individual paddocks being made available for analysis over multiple years. Data used in the current study consisted of dry matter (DM) production and ground score data across a 7-year period from ten perennial ryegrass varieties grown as monocultures in 559 paddocks on 98 commercial farms. The results demonstrated how perennial ryegrass variety is associated with a range of agronomic performance traits on commercial farms; including total and seasonal DM production, grazing DM production and number of grazing events. Varieties with the highest total DM production also had the highest spring and mid-season DM production; autumn DM production was associated with the interaction between variety and year. The highest producing variety in the study, AberGain, produced 1342 kg DM/ha/year more than the mean of all other varieties. Variety differences manifested themselves as swards aged, with some varieties increasing in total DM production while others reduced in total DM production. The current work provides a basis for the consideration of on-farm variety assessment in the composition of future variety evaluation protocols.
The term obesity refers to an excess of body fat. As direct measurements of total body fat mass are complex, body mass index (BMI) is commonly used in clinical practice as an obesity index. BMI may be defined as a person’s weight in kilograms divided by the square of her height in meters (kg/m2). Table 26.1 outlines the BMI categories frequently used in clinical practice, with a BMI ≥30 mg/m2 categorized as obesity [1]. Due to the multiple changes in body mass that take place during pregnancy, pregnant women are typically described based on their pre-pregnancy BMI.
Eating disorders, while relatively rare, have the highest mortality rates of all mental disorders. When combined with diabetes, they have poor outcomes in terms of recurrent diabetic ketoacidosis, premature development of microvascular complications and mortality. Eating disorders are common in diabetes and, where present, are associated with a much higher incidence of diabetic complications and a sevenfold increase in mortality. The term ‘diabulimia’ is increasingly used by patient groups and in the general (and social) media. However, it is not a diagnostic term; there has been no professional agreement regarding what constitutes ‘diabulimia’ or what may constitute a minimum set of criteria for diagnosis. It is important for endocrinologists to have a high index of suspicion for eating disorders in patients with diabetes (especially young women with type 1 diabetes). Psychiatrists need to consider and treat insulin omission as a form of purging in eating disorders.
Over 650 million people live with obesity worldwide, and almost all countries are affected by what is considered a global obesity pandemic. It is one of the factors that contribute to excess premature mortality in patients with severe mental illness, who die 15–20 years younger than the general population. Both obesity and mental health disorders are highly prevalent and frequently occur in the same individual. While weight loss is typically associated with improvements in psychological functioning, a certain proportion of patients will develop new psychological issues or experience a relapse of pre-existing conditions. Further work is needed to clarify the underlying biological mechanisms explaining the relationship between obesity and mental health. In the interim, people with obesity should receive care in a multidisciplinary setting with access to mental healthcare integrated with their obesity care.
Psychological symptoms commonly occur as a result of both thyroid and parathyroid disorders. Epidemiological studies evaluating the association between thyroid function and mood are heterogeneous in design and report varying results. The larger studies demonstrate no effect or an increase in depression with decreasing thyroid-stimulating hormone concentrations. There is growing evidence supporting the fact that thyroid function in psychiatric patients may be affected by the mental disorder itself, as well as by the medications used to treat that illness. Biochemical assessment of thyroid function and calcium concentrations should form part of the baseline assessment in those who present with new psychological symptoms. Once an abnormality is confirmed, further workup and treatment of the underlying endocrine disorder can be expected to alleviate and even reverse the psychological symptoms.
The primary endocrine effectors of the stress response are located in the paraventricular nucleus of the hypothalamus, the anterior lobe of the pituitary gland and the adrenal gland. These structures are referred to as the hypothalamic–pituitary–adrenal (HPA) axis. In the setting of stress, corticotrophin-releasing factor induces the release of adrenocorticotropic hormone, which stimulates the synthesis and secretion of glucocorticoids from the adrenal gland. Glucocorticoids exert a wide range of effects and can influence cardiovascular function, immunity and inflammation, metabolism, reproduction and fluid volume. An important target organ is the brain, where glucocorticoids can affect neuronal differentiation and excitability, behavioral reactivity, mood and cognition. This regulatory system works in conjunction with the sympatho-adrenal medullary system, which releases catecholamines, including noradrenaline and adrenaline. These systems are crucial for dealing with both physiological and psychological stress and restoring our steady state. Inappropriate regulation of the stress response has been linked to a wide array of pathologies, including hypertension, diabetes, osteoporosis and psychological disorders. In this chapter, we will focus on disorders of the HPA axis and their effects on mental health.
Antipyschotic medications have benefited countless people with a wide variety of pyschiatric disorders. However, they do have potential to induce metabolic disturbances in a population that is known to have a high risk of cardiovascular disease. This can result in the development of metabolic syndrome and associated complications. There is a strong association between the presence of metabolic syndrome and developing type 2 diabetes. Patients with severe mental illness are at increased risk for metabolic syndrome, diabetes and cardiovascular disease. This is likely due to a number of factors, including higher rates of smoking, poor diet and disordered lifestyle with minimal physical activity. In addition, this population is less likely to receive prompt diagnosis and treatment for modifiable risk factors such as hypertension, dyslipidaemia and prediabetes. Overall, second-generation antipsychotic agents have a stronger association with these adverse effects compared to their first-generation counterparts, and previously untreated patients are at highest risk. With this in mind, healthcare professionals and patients should be well informed on this issue and institute close monitoring and prompt treatment of at-risk individuals.
Mental Health, Diabetes and Endocrinology examines the main areas of clinical overlap between endocrinology and mental health to address key clinical conundrums. Drawing on the most recent developments from literature and clinical practice, this book gives specific attention to the main areas where clinical conundrums and treatment challenges arise across endocrinology, psychiatry, psychology and primary care. Common challenges in this area include depression which can impact on the person's ability to self-care and to adhere to treatment with consequences for their morbidity and mortality; 'diabulaemia' associated with high mortality rates; obesity and associated mental disorders; cognitive impairment and mental capacity; anti-psychotic medications and their endocrine sequelae; and specific setting-related considerations. Mental Health, Diabetes and Endocrinology is a useful resource for the overlapping conditions across these specialities, and provides clinically-focussed evidence-based resources for all health care professionals who encounter these issues.
The observation that 64% of English adults are overweight or obese despite a rising prevalence in weight-loss attempts suggests our understanding of energy balance is fundamentally flawed. Weight-loss is induced through a negative energy balance; however, we typically view weight change as a static function, in that energy intake and energy expenditure are independent variables, resulting in a fixed rate of weight-loss assuming a constant energy deficit. Such static modelling provides the basis for the clinical assumption that a 14644 kJ (3500 kcal) deficit translates to a 1 lb weight-loss. However, this ‘3500 kcal (14644 kJ) rule’ is consistently shown to significantly overestimate weight-loss. Static modelling disregards obligatory changes in energy expenditure associated with the loss of metabolically active tissue, i.e. skeletal muscle. Additionally, it disregards the presence of adaptive thermogenesis, the underfeeding-associated fall in resting energy expenditure beyond that caused by loss of fat-free mass. This metabolic manipulation of energy expenditure is observed from the onset of energy restriction to maintain weight at a genetically pre-determined set point. As a result, the observed magnitude of weight-loss is disproportionally less, followed by earlier weight plateau, despite strict compliance to a dietary intervention. By simulating dynamic changes in energy expenditure associated with underfeeding, mathematical modelling may provide a more accurate method of weight-loss prediction. However, accuracy at an individual level is limited due to difficulty estimating energy requirements, physical activity and dietary intake in free-living individuals. In the present paper, we aim to outline the contribution of dynamic changes in energy expenditure to weight-loss resistance and weight plateau.
Trypanosomes are blood-borne parasites that can infect a variety of different vertebrates, including animals and humans. This study aims to broaden scientific knowledge about the presence and biodiversity of trypanosomes in Australian bats. Molecular and morphological analysis was performed on 86 blood samples collected from seven different species of microbats in Western Australia. Phylogenetic analysis on 18S rDNA and glycosomal glyceraldehyde phosphate dehydrogenase (gGAPDH) sequences identified Trypanosoma dionisii in five different Australian native species of microbats; Chalinolobus gouldii, Chalinolobus morio, Nyctophilus geoffroyi, Nyctophilus major and Scotorepens balstoni. In addition, two novels, genetically distinct T. dionisii genotypes were detected and named T. dionisii genotype Aus 1 and T. dionisii genotype Aus 2. Genotype Aus 2 was the most prevalent and infected 20.9% (18/86) of bats in the present study, while genotype Aus 1 was less prevalent and was identified in 5.8% (5/86) of Australian bats. Morphological analysis was conducted on trypomastigotes identified in blood films, with morphological parameters consistent with trypanosome species in the subgenus Schizotrypanum. This is the first report of T. dionisii in Australia and in Australian native bats, which further contributes to the global distribution of this cosmopolitan bat trypanosome.