Postbariatric surgery hypoglycemia: Nutritional, pharmacological and surgical perspectives

Post-bariatric hypoglycaemia (PBH) is a metabolic complication of bariatric surgery (BS), consisting of low post-prandial glucose levels in patients having undergone bariatric procedures. While BS is currently the most effective and relatively safe treatment for obesity and its complications, the development of PBH can significantly impact patients' quality of life and mental health. The diagnosis of PBH is still challenging, considering the lack of definitive and reliable diagnostic tools, and the fact that this condition is frequently asymptomatic. However, PBH's prevalence is alarming, involving up to 88% of the post-bariatric population, depending on the diagnostic tool, and this may be underestimated. Given the prevalence of obesity soaring, and an increasing number of bariatric procedures being performed, it is crucial that physicians are skilled to diagnose PBH and promptly treat patients suffering from it. While the milestone of managing this condition is nutritional therapy, growing evidence suggests that old and new pharmacological approaches may be adopted as adjunct therapies for managing this complex condition.

Nourishing the gut: the impact of diet on host-gut microbiota interaction

PURPOSE OF REVIEW

Understanding the spectrum of drivers that influence the gut microbiome (GM) remains a crucial field of investigation. Among these factors, diet has received particular attention, as it could explain up to 20% of the variability in GM composition between individuals. This review focuses on the complex relationships between different dietary patterns and GM in humans, based on recent findings.

RECENT FINDINGS

Current evidence underscores the multifaceted impact of diet on GM richness, diversity, and overall composition. Key contributing factors encompass dietary habits, nutritional interventions, food quality and variety, macronutrient distribution, timing of feeding, and selective exclusion of certain foods.

SUMMARY

The intricate interplay between diet and GM is of fundamental importance in shaping the interaction between the host and the environment. Further understanding the causal impact of diet on GM has promising potential for the advancement of strategies to promote health and mitigate cardio-metabolic disease risks through dietary interventions.

GRAPHICAL ABSTRACT

http://links.lww.com/COCN/A21.

Upper small intestine microbiome in obesity and related metabolic disorders: A new field of investigation

The study of the gut microbiome holds great promise for understanding and treating metabolic diseases, as its functions and derived metabolites can influence the metabolic status of the host. While research on the fecal microbiome has provided valuable insights, it tells us only part of the story. This limitation arises from the substantial variations in microorganism distribution throughout the gastrointestinal tract due to changes in physicochemical conditions. Thus, relying solely on the fecal microbiome may not be sufficient to draw comprehensive conclusions about metabolic diseases. The proximal part of the small intestine, particularly the jejunum, indeed, serves as the crucial site for digestion and absorption of nutrients, suggesting a potential role of its microbiome in metabolic regulation. Unfortunately, it remains relatively underexplored due to limited accessibility. This review presents current evidence regarding the relationships between the microbiome in the upper small intestine and various phenotypes, focusing on obesity and type 2 diabetes, in both humans and rodents. Research on humans is still limited with variability in the population and methods used. Accordingly, to better understand the role of the whole gut microbiome in metabolic diseases, studies exploring the human microbiome in different niches are needed.

Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group

Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal attainment represents an unmet clinical need. To address this knowledge gap, we conducted a real-world analysis of the lipid profiles of 11.252 patients from the Annals of the Italian Association of Medical Diabetologists (AMD) database from 2005 to 2019. We used a Logic Learning Machine (LLM) to extract and classify the most relevant variables predicting the achievement of a low-density lipoprotein cholesterol (LDL-C) value lower than 100 mg/dL (2.60 mmol/L) within two years of the start of lipid-lowering therapy. Our analysis showed that 61.4% of the patients achieved the treatment goal. The LLM model demonstrated good predictive performance, with a precision of 0.78, accuracy of 0.69, recall of 0.70, F1 Score of 0.74, and ROC-AUC of 0.79. The most significant predictors of achieving the treatment goal were LDL-C values at the start of lipid-lowering therapy and their reduction after six months. Other predictors of a greater likelihood of reaching the target included high-density lipoprotein cholesterol, albuminuria, and body mass index at baseline, as well as younger age, male sex, more follow-up visits, no therapy discontinuation, higher Q-score, lower blood glucose and HbA1c levels, and the use of anti-hypertensive medication. At baseline, for each LDL-C range analysed, the LLM model also provided the minimum reduction that needs to be achieved by the next six-month visit to increase the likelihood of reaching the therapeutic goal within two years. These findings could serve as a useful tool to inform therapeutic decisions and to encourage further in-depth analysis and testing.

Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity

The full spectrum of SARS-CoV-2-infected patients has not yet been defined. This study aimed to evaluate which parameters derived from CT, inflammatory, and hormonal markers could explain the clinical variability of COVID-19. We performed a retrospective study including SARS-CoV-2-infected patients hospitalized from March 2020 to May 2021 at the Umberto I Polyclinic of Rome. Patients were divided into four groups according to the degree of respiratory failure. Routine laboratory examinations, BMI, liver steatosis indices, liver CT attenuation, ferritin, and IGF-1 serum levels were assessed and correlated with severity. Analysis of variance between groups showed that patients with worse prognoses had higher BMI and ferritin levels, but lower liver density, albumin, GH, and IGF-1. ROC analysis confirmed the prognostic accuracy of IGF-1 in discriminating between patients who experienced death/severe respiratory failure and those who did not (AUC 0.688, CI: 0.587 to 0.789, p < 0.001). A multivariate analysis considering the degrees of severity of the disease as the dependent variable and ferritin, liver density, and the standard deviation score of IGF-1 as regressors showed that all three parameters were significant predictors. Ferritin, IGF-1, and liver steatosis account for the increased risk of poor prognosis in COVID-19 patients with obesity.

Growth Hormone Secretory Capacity Is Associated with Cardiac Morphology and Function in Overweight and Obese Patients: A Controlled, Cross-Sectional Study

Obesity is associated with increased cardiovascular morbidity. Adult patients with growth hormone deficiency (GHD) show morpho-functional cardiological alterations. A total of 353 overweight/obese patients are enrolled in the period between 2009 and 2019 to assess the relationships between GH secretory capacity and the metabolic phenotype, cardiovascular risk factors, body composition and cardiac echocardiographic parameters. All patients underwent GHRH + arginine test to evaluate GH secretory capacity, DEXA for body composition assessment and transthoracic echocardiography. Blood samples are also collected for the evaluation of metabolic parameters. In total, 144 patients had GH deficiency and 209 patients had normal GH secretion. In comparing the two groups, we found significant differences in body fat distribution with predominantly visceral adipose tissue accumulation in GHD patients. Metabolic syndrome is more prevalent in the GHD group. In particular, fasting glycemia, triglycerides and systolic and diastolic blood pressure are found to be linearly correlated with GH secretory capacity. Epicardial fat thickness, E/A ratio and indexed ventricular mass are worse in the GHD group. In the population studied, metabolic phenotype, body composition, cardiovascular risk factors and cardiac morphology are found to be related to the GH secretory capacity. GH secretion in the obese patient seems to be an important determinant of metabolic health.

Circulating SIRT1 and Sclerostin Correlates with Bone Status in Young Women with Different Degrees of Adiposity

Sirtuin1 (SIRT1) and sclerostin play important roles in adipose tissue and bone metabolism. We evaluated the circulating SIRT1 and sclerostin relationship with mass and quality of bone while considering the degree of adiposity. Sixty-six premenopausal women (16 underweight, 25 normal weight and 25 with obesity), aged <50 years, were enrolled. Plasma SIRT1, sclerostin and DXA body composition (total fat mass (FM), abdominal visceral adipose tissue, lean mass, trabecular bone score (TBS) and lumbar spine and femoral neck (FN) bone mineral density (BMD)) were assessed. The patients with obesity showed the lowest SIRT1 and TBS values and the highest sclerostin concentrations; BMD increased with FM and BMI and had an inverse association with SIRT1. Sclerostin was negatively correlated with SIRT1 (ρ = −0.37, p = 0.002). When spine BMD, FN BMD and TBS were standardized for BMI, a positive correlation with SIRT1 and a negative correlation with sclerostin were seen (p < 0.005). In the regression analysis, sclerostin was the best independent, negative predictor for BMD and TBS, while SIRT1 directly predicted TBS (p < 0.05). In conclusion, blood measurement of SIRT1 and sclerostin could represent a snapshot of the bone status that, taking into account the degree of adiposity, may reduce the interference of confounding factors in the interpretation of bone health parameters.

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology.

Rapid Weight Loss, Central Obesity Improvement and Blood Glucose Reduction Are Associated with a Stronger Adaptive Immune Response Following COVID-19 mRNA Vaccine

Obesity is associated with a poor COVID-19 prognosis, and it seems associated with reduced humoral response to vaccination. Public health campaigns have advocated for weight loss in subjects with obesity, hoping to eliminate this risk. However, no evidence proves that weight loss leads to a better prognosis or a stronger immune response to vaccination. We aimed to investigate the impact of rapid weight loss on the adaptive immune response in subjects with morbid obesity. Twenty-one patients followed a hypocaloric, very-low-carbohydrate diet one week before to one week after the two mRNA vaccine doses. The diet’s safety and efficacy were assessed, and the adaptive humoral (anti-SARS CoV-2 S antibodies, Abs) and cell-mediated responses (IFNγ secretion on stimulation with two different SARS CoV-2 peptide mixes, IFNγ-1 and IFNγ-2) were evaluated. The patients lost ~10% of their body weight with metabolic improvement. A high baseline BMI correlated with a poor immune response (R −0.558, p = 0.013 for IFNγ-1; R −0.581, p = 0.009 for IFNγ-2; R −0.512, p = 0.018 for Abs). Furthermore, there was a correlation between weight loss and higher IFNγ-2 (R 0.471, p = 0.042), and between blood glucose reduction and higher IFNγ-1 (R 0.534, p = 0.019), maintained after weight loss and waist circumference reduction adjustment. Urate reduction correlated with higher Abs (R 0.552, p = 0.033). In conclusion, obesity is associated with a reduced adaptive response to a COVID-19 mRNA vaccine, and weight loss and metabolic improvement may reverse the effect.

Central obesity, smoking habit, and hypertension are associated with lower antibody titres in response to COVID-19 mRNA vaccine

AIMS

To explore variables associated with the serological response following COVID-19 mRNA vaccine.

METHODS

Eighty-six healthcare workers adhering to the vaccination campaign against COVID-19 were enrolled in January-February 2021. All subjects underwent two COVID-19 mRNA vaccine inoculations (Pfizer/BioNTech) separated by 3 weeks. Blood samples were collected before the 1st and 1-4 weeks after the second inoculation. Clinical history, demographics, and vaccine side effects were recorded. Baseline anthropometric parameters were measured, and body composition was performed through dual-energy-X-ray absorptiometry.

RESULTS

Higher waist circumference was associated with lower antibody (Ab) titres (R = -0.324, p = 0.004); smokers had lower levels compared to non-smokers [1099 (1350) vs. 1921 (1375), p = 0.007], as well as hypertensive versus normotensive [650 ± 1192 vs. 1911 (1364), p = 0.001] and dyslipideamic compared to those with normal serum lipids [534 (972) vs 1872 (1406), p = 0.005]. Multivariate analysis showed that higher waist circumference, smoking, hypertension, and longer time elapsed since second vaccine inoculation were associated with lower Ab titres, independent of BMI, age. and gender.

CONCLUSIONS

Central obesity, hypertension, and smoking are associated with lower Ab titres following COVID-19 vaccination. Although it is currently impossible to determine whether lower SARS-CoV-2 Abs lead to higher likelihood of developing COVID-19, it is well-established that neutralizing antibodies correlate with protection against several viruses including SARS-CoV-2. Our findings, therefore, call for a vigilant approach, as subjects with central obesity, hypertension, and smoking could benefit from earlier vaccine boosters or different vaccine schedules.

Is Growth Hormone Insufficiency the Missing Link Between Obesity, Male Gender, Age, and COVID-19 Severity?

Evidence has emerged regarding an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with worse prognosis in elderly male patients with obesity, and blunted growth hormone (GH) secretion represents a feature of this population subgroup. Here, a comprehensive review of the possible links between GH-insulinlike growth factor 1 axis impairment and coronavirus disease 2019 (COVID-19) severity is offered. First, unequivocal evidence suggests that immune system dysregulation represents a key element in determining SARS-CoV-2 severity, as well as the association with adult-onset GH deficiency (GHD); notably, if GH is physiologically involved in the development and maintenance of the immune system, its pharmacological replacement in GHD patients seems to positively influence their inflammatory status. In addition, the impaired fibrinolysis associated with GHD may represent a further link between GH-insulin-like growth factor 1 axis impairment and COVID-19 severity, as it has been associated with both conditions. In conclusion, several sources of evidence have supported a relationship between GHD and COVID-19, and they also shed light upon potential beneficial effects of recombinant GH treatment on COVID-19 patients.

Is obesity the missing link between COVID-19 severity and air pollution?

In the previous publication "Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?" Conticini et al. hypothesized that the surplus of lethality of the novel SARS-CoV-2 in Northern Italy may be at least in part explained by the evidence of highest pollution reported in this area, as both severe COVID-19 and smog exposure are correlated to an innate immune system hyper-activation with subsequent lung inflammation and injury. Since this hypothesis alone does not fully explain why specific subgroups of patients are at major risk, we hypothesized that obesity may be one of the links between COVID-19 severity and high level of air pollution. First, obesity is a predisposing factor for SARS-Cov-2 infection and worse COVID-19 outcomes, and unequivocal evidence demonstrated that fat mass excess is independently associated with several pulmonary diseases and lung inflammation. Moreover, it has been shown that obesity may intensify the detrimental effects of air pollution on the lungs, and this is not surprising if we consider that these conditions share an excessive activation of the immune system and a lung inflammatory infiltrate. Finally, fat mass excess has also been speculated to be itself a consequence of air pollutants exposure, which has been proved to induce metabolic disruption and weight gain in murine models. In conclusion, although many variables must be taken into account in the analysis of the pandemic, our observations suggest that obesity may act as effect modifier of smog-induced lung-injury, and the concomitant presence of these two factors could better explain the higher virulence, faster spread and greater mortality of SARS-CoV-2 in Northern Italy compared to the rest of the country.

Current Evidence to Propose Different Food Supplements for Weight Loss: A Comprehensive Review

The use of food supplements for weight loss purposes has rapidly gained popularity as the prevalence of obesity increases. Navigating through the vast, often low quality, literature available is challenging, as is providing informed advice to those asking for it. Herein, we provide a comprehensive literature revision focusing on most currently marketed dietary supplements claimed to favor weight loss, classifying them by their purported mechanism of action. We conclude by proposing a combination of supplements most supported by current evidence, that leverages all mechanisms of action possibly leading to a synergistic effect and greater weight loss in the foreseen absence of adverse events. Further studies will be needed to confirm the weight loss and metabolic improvement that may be obtained through the use of the proposed combination.

Prevalence of depression in a 12-month consecutive sample of patients with ALS

There is an impression both in clinical practice and in research literature that patients with amyotrophic lateral sclerosis (ALS) possess 'heroic stoicism with a low frequency of depression'. Reliance on specific interview methods may have contributed to differing estimates of mood disorder in people with ALS. The objective of the current study was to compare prevalence rates of depression and anxiety in ALS using different assessment tools. The Beck Depression Inventory (BDI), The Hospital Anxiety and Depression Scale (HADS) and the Spielberger State-Trait Anxiety Inventory (STAI) were sent to a 12-month consecutive sample of 190 patients with ALS attending a tertiary referral clinic in the UK. Data were collected from 104 patients with ALS. Using BDI scores, 44% were categorized as not depressed, 37% were mild-moderately depressed, 13% were moderately-severely depressed, and 6% were severely depressed. In contrast, the HADS depression subscale identified 75% as not depressed, 13% were in the borderline range, and 13% were categorized as meeting 'caseness' for depression. Twenty-five percent of the patients were using antidepressant medication. The estimated prevalence of mood disorder amongst patients with ALS may vary significantly depending on the measure used.