Adrian Soto-Mota

Bio: Adrian Soto-Mota is an academic researcher from University of Oxford. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 3, co-authored 12 publications receiving 70 citations.

Why a d-β-hydroxybutyrate monoester?

Abstract: Much of the world's prominent and burdensome chronic diseases, such as diabetes, Alzheimer's, and heart disease, are caused by impaired metabolism. By acting as both an efficient fuel and a powerful signalling molecule, the natural ketone body, d-β-hydroxybutyrate (βHB), may help circumvent the metabolic malfunctions that aggravate some diseases. Historically, dietary interventions that elevate βHB production by the liver, such as high-fat diets and partial starvation, have been used to treat chronic disease with varying degrees of success, owing to the potential downsides of such diets. The recent development of an ingestible βHB monoester provides a new tool to quickly and accurately raise blood ketone concentration, opening a myriad of potential health applications. The βHB monoester is a salt-free βHB precursor that yields only the biologically active d-isoform of the metabolite, the pharmacokinetics of which have been studied, as has safety for human consumption in athletes and healthy volunteers. This review describes fundamental concepts of endogenous and exogenous ketone body metabolism, the differences between the βHB monoester and other exogenous ketones and summarises the disease-specific biochemical and physiological rationales behind its clinical use in diabetes, neurodegenerative diseases, heart failure, sepsis related muscle atrophy, migraine, and epilepsy. We also address the limitations of using the βHB monoester as an adjunctive nutritional therapy and areas of uncertainty that could guide future research.

The low-harm score for predicting mortality in patients diagnosed with COVID-19: A multicentric validation study.

Abstract: Objective We sought to determine the accuracy of the LOW-HARM score (Lymphopenia, Oxygen saturation, White blood cells, Hypertension, Age, Renal injury, and Myocardial injury) for predicting death from coronavirus disease 2019) COVID-19. Methods We derived the score as a concatenated Fagan's nomogram for Bayes theorem using data from published cohorts of patients with COVID-19. We validated the score on 400 consecutive COVID-19 hospital admissions (200 deaths and 200 survivors) from 12 hospitals in Mexico. We determined the sensitivity, specificity, and predictive values of LOW-HARM for predicting hospital death. Results LOW-HARM scores and their distributions were significantly lower in patients who were discharged compared to those who died during their hospitalization 5 (SD: 14) versus 70 (SD: 28). The overall area under the curve for the LOW-HARM score was 0.96, (95% confidence interval: 0.94-0.98). A cutoff > 65 points had a specificity of 97.5% and a positive predictive value of 96%. Conclusions The LOW-HARM score measured at hospital admission is highly specific and clinically useful for predicting mortality in patients with COVID-19.

Prospective predictive performance comparison between Clinical Gestalt and validated COVID-19 mortality scores.

Abstract: BackgroundMost COVID-19 mortality scores were developed in the early months of the pandemic and now available evidence-based interventions have helped reduce its lethality. It has not been evaluated if the original predictive performance of these scores holds true nor compared it against Clinical Gestalt predictions. We tested the current predictive accuracy of six COVID-19 scores and compared it with Clinical Gestalt predictions. Methods200 COVID-19 patients were enrolled in a tertiary hospital in Mexico City between September and December 2020. Clinical Gestalt predictions of death (as a percentage) and LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV and NEWS2 were obtained at admission. We calculated the AUC of each score and compared it against Clinical Gestalt predictions and against their respective originally reported value. Results106 men and 60 women aged 56+/-9 and with confirmed COVID-19 were included in the analysis. The observed AUC of all scores was significantly lower than originally reported; LOW-HARM 0.96 (0.94-0.98) vs 0.76 (0.69-0.84), qSOFA 0.74 (0.65-0.81) vs 0.61 (0.53-0.69), MSL-COVID-19 0.72 (0.69-0.75) vs 0.64 (0.55-0.73) NUTRI-CoV 0.79 (0.76-0.82) vs 0.60 (0.51-0.69), NEWS2 0.84 (0.79-0.90) vs 0.65 (0.56-0.75), Neutrophil-Lymphocyte ratio 0.74 (0.62-0.85) vs 0.65 (0.57-0.73). Clinical Gestalt predictions were non-inferior to mortality scores (AUC=0.68 (0.59-0.77)). Adjusting the LOW-HARM score with locally derived likelihood ratios did not improve its performance. However, some scores performed better than Clinical Gestalt predictions when clinicians confidence of prediction was <80%. ConclusionNo score was significantly better than Clinical Gestalt predictions. Despite its subjective nature, Clinical Gestalt has relevant advantages for predicting COVID-19 clinical outcomes.

Prospective predictive performance comparison between clinical gestalt and validated COVID-19 mortality scores.

Abstract: Most COVID-19 mortality scores were developed at the beginning of the pandemic and clinicians now have more experience and evidence-based interventions. Therefore, we hypothesized that the predictive performance of COVID-19 mortality scores is now lower than originally reported. We aimed to prospectively evaluate the current predictive accuracy of six COVID-19 scores and compared it with the accuracy of clinical gestalt predictions. 200 patients with COVID-19 were enrolled in a tertiary hospital in Mexico City between September and December 2020. The area under the curve (AUC) of the LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV, and NEWS2 scores and the AUC of clinical gestalt predictions of death (as a percentage) were determined. In total, 166 patients (106 men and 60 women aged 56±9 years) with confirmed COVID-19 were included in the analysis. The AUC of all scores was significantly lower than originally reported: LOW-HARM 0.76 (95% CI 0.69 to 0.84) vs 0.96 (95% CI 0.94 to 0.98), qSOFA 0.61 (95% CI 0.53 to 0.69) vs 0.74 (95% CI 0.65 to 0.81), MSL-COVID-19 0.64 (95% CI 0.55 to 0.73) vs 0.72 (95% CI 0.69 to 0.75), NUTRI-CoV 0.60 (95% CI 0.51 to 0.69) vs 0.79 (95% CI 0.76 to 0.82), NEWS2 0.65 (95% CI 0.56 to 0.75) vs 0.84 (95% CI 0.79 to 0.90), and neutrophil to lymphocyte ratio 0.65 (95% CI 0.57 to 0.73) vs 0.74 (95% CI 0.62 to 0.85). Clinical gestalt predictions were non-inferior to mortality scores, with an AUC of 0.68 (95% CI 0.59 to 0.77). Adjusting scores with locally derived likelihood ratios did not improve their performance; however, some scores outperformed clinical gestalt predictions when clinicians’ confidence of prediction was

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Effects of Ketone Bodies on Brain Metabolism and Function in Neurodegenerative Diseases.

Abstract: Under normal physiological conditions the brain primarily utilizes glucose for ATP generation. However, in situations where glucose is sparse, e.g., during prolonged fasting, ketone bodies become an important energy source for the brain. The brain’s utilization of ketones seems to depend mainly on the concentration in the blood, thus many dietary approaches such as ketogenic diets, ingestion of ketogenic medium-chain fatty acids or exogenous ketones, facilitate significant changes in the brain’s metabolism. Therefore, these approaches may ameliorate the energy crisis in neurodegenerative diseases, which are characterized by a deterioration of the brain’s glucose metabolism, providing a therapeutic advantage in these diseases. Most clinical studies examining the neuroprotective role of ketone bodies have been conducted in patients with Alzheimer’s disease, where brain imaging studies support the notion of enhancing brain energy metabolism with ketones. Likewise, a few studies show modest functional improvements in patients with Parkinson’s disease and cognitive benefits in patients with—or at risk of—Alzheimer’s disease after ketogenic interventions. Here, we summarize current knowledge on how ketogenic interventions support brain metabolism and discuss the therapeutic role of ketones in neurodegenerative disease, emphasizing clinical data.

The metabolic face of migraine — from pathophysiology to treatment

Abstract: Migraine can be regarded as a conserved, adaptive response that occurs in genetically predisposed individuals with a mismatch between the brain's energy reserve and workload. Given the high prevalence of migraine, genotypes associated with the condition seem likely to have conferred an evolutionary advantage. Technological advances have enabled the examination of different aspects of cerebral metabolism in patients with migraine, and complementary animal research has highlighted possible metabolic mechanisms in migraine pathophysiology. An increasing amount of evidence - much of it clinical - suggests that migraine is a response to cerebral energy deficiency or oxidative stress levels that exceed antioxidant capacity and that the attack itself helps to restore brain energy homeostasis and reduces harmful oxidative stress levels. Greater understanding of metabolism in migraine offers novel therapeutic opportunities. In this Review, we describe the evidence for abnormalities in energy metabolism and mitochondrial function in migraine, with a focus on clinical data (including neuroimaging, biochemical, genetic and therapeutic studies), and consider the relationship of these abnormalities with the abnormal sensory processing and cerebral hyper-responsivity observed in migraine. We discuss experimental data to consider potential mechanisms by which metabolic abnormalities could generate attacks. Finally, we highlight potential treatments that target cerebral metabolism, such as nutraceuticals, ketone bodies and dietary interventions.

The Ketogenic Diet for Obesity and Diabetes—Enthusiasm Outpaces Evidence

Abstract: The ketogenic diet has recently received much attention for its promise of treating obesity and type 2 diabetes. However, the enthusiasm for its potential benefits exceeds the current evidence supporting its use for these conditions. Although the temptation is great to recommend a potentially novel approach for otherwise difficult-to-treat diseases, it is important to remain grounded in our appraisal of the risks, benefits, and applicability of the diet to avoid unnecessary harm and costs to patients. The ketogenic diet, or keto diet, emerged in popularity after a recent series of other low-carbohydrate diets, such as the Paleo and Atkins diets. The ketogenic diet is unique from other low-carbohydrate diets in that followers of the diet are encouraged to forgo nearly all carbohydrates, avoid excess protein, and consume high levels of fat (generally exceeding 70% of calories consumed), resulting in the production of ketones, giving the diet its name. The excitement for low-carbohydrate diets comes on the heels of what some have considered the failure of a low-fat diet to curb the obesity epidemic and its associated increase in type 2 diabetes. This enthusiasm is belied by the fact that the modern American diet is not truly low in fat (defined as less than 30% of total calories). Of more importance, from the early 1970s to the early 2000s, Americans increased total energy consumption by at least 240 calories per day (estimates vary by method and source), likely contributing to weight gain and the increased incidence of diabetes. Is the ketogenic diet more effective for weight loss than other diets? In a meta-analysis of 13 studies lasting longer than a year, researchers found that the ketogenic diet was associated with less than a kilogram of additional weight loss over high-carbohydrate, low-fat strategies.1 This difference, although statistically significant, may not be clinically significant. Furthermore, a meta-analysis of 32 controlled feeding studies found that energy expenditure and fat loss were greater with low-fat diets compared with ketogenic diets.2 Any diet that results in weight loss does so because it reduces calorie intake. The ketogenic diet, when used for weight loss, is no different. The salient questions are whether it is sustainable and whether it promotes long-term health. No studies, to our knowledge, have evaluated ketogenic diets for cardiovascular events or mortality, although observational studies in the broader low-carbohydrate diet literature suggest increased all-cause mortality.3 What about the role of a ketogenic diet in the treatment of type 2 diabetes? One well-publicized, nonrandomized study of the ketogenic diet in persons with type 2 diabetes showed a 1.3% reduction in glycosylated hemoglobin at 1 year in the ketogenic group.4 These findings must be interpreted with caution, however, because the ketosis group was self-selected and received intensive technological and behavioral support not offered to the control group. Long-term ( 1 year) randomized studies tell a different story. A meta-analysis of randomized long-term studies comparing the ketogenic diet with low-fat diets for weight loss reported no difference in glycemic control among persons with type 2 diabetes.1 Type 2 diabetes is characterized by carbohydrate intolerance due to insulin resistance. Restriction of carbohydrates (as in the ketogenic diet) can transiently improve glycemic control, and weight loss by any means can improve insulin resistance. However, there is little if any evidence that ketogenic diets specifically improve carbohydrate intolerance independent of weight loss, unlike other dietary approaches in which glycemic control is improved despite the consumption of healthful carbohydrate-rich foods, such as legumes, whole grains, and fruits, even in the absence of weight loss. Are there other possible benefits of a ketogenic diet? The ketogenic diet has been touted to have favorable effects on cardiovascular risk factors, such as serum lipid levels. However, evidence suggests that low-density lipoprotein cholesterol and apo-B–containing lipoprotein levels may fail to improve, or even significantly increase, with a ketogenic diet despite weight loss.5 Although there may be a concurrent increase in highdensity lipoprotein cholesterol level with a ketogenic diet, historically, various interventions used to increase high-density lipoprotein cholesterol level have not translated into reductions in cardiovascular events. In terms of the risk-benefit balance of the ketogenic diet, the potential adverse effects may give one pause. A review of the literature6,7 on ketogenic diets for the treatment of pediatric epilepsy reveals multiple adverse effects, ranging from the relatively benign but inconvenient “keto flu,” an induction period of fatigue, weakness, and gastrointestinal disturbances, to the less common but deadlier occurrence of cardiac arrhythmias from selenium deficiency. Other documented adverse effects include nephrolithiasis, constipation, halitosis, muscle cramps, headaches, diarrhea, restricted growth, bone fractures, pancreatitis, and multiple vitamin and mineral deficiencies. The greatest risk, however, of the ketogenic diet may be the one most overlooked: the opportunity cost of not eating high-fiber, unrefined carbohydrates. Whole grains, fruits,andlegumesaresomeofthemosthealth-promoting foods on the planet. They are not responsible for the epidemics of type 2 diabetes or obesity, and their avoidance VIEWPOINT