In recent years, the terms sarcopenia, sarcopenic obesity, and osteosarcopenic obesity (OSO) were coined to define a situation in elderly people strongly associated with frailty and increased mortality. Possibly, a complex interplay of several hormones and cytokines are involved in its development. Ongoing research detected that OSO may occur at any age and in several conditions. The prevalence of OSO in alcoholism was poorly analyzed. The aim of this study was to analyze the prevalence of OSO in alcoholism and its relationship with proinflammatory cytokines and/or common complications of alcoholism, such as cirrhosis, cancer, or vascular disease. We included 115 patients with alcoholic use disorder. Body composition analysis was performed by double X-ray absorptiometry. Handgrip strength was recorded using a dynamometer. We assessed liver function according to Child’s classification, and determined serum levels of proinflammatory cytokines (TNF-α, IL-6, IL-8), routine laboratory variables, and vitamin D. People with alcoholic use disorder showed a high prevalence of OSO, especially regarding OSO obesity (60%), OSO osteopenia (55.65%), and OSO lean mass (60.17%). OSO handgrip was closely, independently, related to the presence of vascular calcification (χ² = 17.00; p < 0.001). OSO handgrip was related to several proinflammatory cytokines and vitamin D. Vitamin D deficiency kept a close correlation with OSO handgrip (rho = −0.54, p < 0.001). Therefore, among people with alcohol use disorder, OSO prevalence was high. OSO handgrip is related to serum proinflammatory cytokine levels supporting the possible pathogenetic role of these cytokines on OSO development. Vitamin D deficiency is related to OSO handgrip suggesting its pathogenetic involvement in sarcopenia in patients with alcohol use disorder. The close association between OSO handgrip and vascular calcification is clinically relevant and suggests that OSO handgrip may constitute a prognostic tool in these patients.

Background: Describe the profile of patients with obesity in internal medicine to determine the role of adiposity and related inflammation on the metabolic risk profile and, identify various “high-risk obesity” phenotypes by means of a cluster analysis. This study aimed to identify different profiles of patients with high-risk obesity based on a cluster analysis. Methods: Cross-sectional, multicenter project that included outpatients attended to in internal medicine. A total of 536 patients were studied. The mean age was 62 years, 51% were women. Patients were recruited from internal medicine departments over two weeks in November and December 2021 and classified into four risk groups according to body mass index (BMI) and waist circumference (WC). High-risk obesity was defined as BMI > 35 Kg/m² or BMI 30–34.9 Kg/m² and a high WC (>102 cm for men and >88 cm for women). Hierarchical and partitioning clustering approaches were performed to identify profiles. Results: A total of 462 (86%) subjects were classified into the high-risk obesity group. After excluding 19 patients missing critical data, two profiles emerged: cluster 1 (n = 396) and cluster 2 (n = 47). Compared to cluster 1, cluster 2 had a worse profile, characterized by older age (77 ± 16 vs. 61 ± 21 years, p < 0.01), a Charlson Comorbidity Index > 3 (53% vs. 5%, p < 0.001), depression (36% vs. 19%, p = 0.008), severe disability (64% vs. 3%, p < 0.001), and a sarcopenia score ≥ 4 (79% vs. 16%, p < 0.01). In addition, cluster 2 had greater inflammation than cluster 1 (hsCRP: 5.8 ± 4.1 vs. 2.1 ± 4.5 mg/dL, p = 0.008). Conclusions: Two profiles of subjects with high-risk obesity were identified. Based on that, older subjects with obesity require measures that target sarcopenia, disability, psychological health, and significant comorbidities to prevent further health deterioration. Longitudinal studies should be performed to identify potential risk factors of subjects who progress from cluster 1 to cluster 2.

Myostatin acts as a negative regulator of muscle growth. Its effect on fat mass is subject to debate. Among alcoholics, there is a high prevalence of muscle atrophy, and increased fat deposition has been also described in these patients. Myostatin could be involved in these alterations, but its relationships with body composition have been scarcely studied in alcoholic patients. To analyze the behavior of myostatin among alcoholics and its relationship with alcohol intake, liver function, and body composition. We investigated serum myostatin in 59 male patients and 18 controls. Patients were all heavy drinkers admitted with organic complications related to excessive ethanol ingestion. Densitometry analysis was used to assess body composition in 46 patients. Handgrip was assessed in 51 patients. Patients showed lower myostatin values than controls (Z = 3.80; p < 0.001). There was a significant relationship between myostatin and fat at the right leg (ρ = 0.32; p = 0.028), left leg (ρ = 0.32; p = 0.028), trunk (ρ = 0.31, p = 0.038), total fat proport ion (ρ = 0.33, p = 0.026), and gynecoid fat distribution (ρ = 0.40, p = 0.006) but not with lean mass (total lean ρ = 0.07; p = 0.63; trunk lean ρ = 0.03; p = 0.85; lower limbs ρ = 0.08; p = 0.58; upper limbs ρ = 0.04 p = 0.82; android ρ = 0.02; p = 0.88, or gynoid lean mass ρ = 0.20; p = 0.19). In total, 80.43% of patients showed at least one criterion of osteosarcopenic adiposity (OSA). Myostatin was related to OSA obesity. We also observed higher myostatin values among patients with body mass index > 30 kg/m². Serum myostatin was lower among excessive drinkers, and it was related to increased fat deposition among these patients but not to lean mass, handgrip, or bone mineral density.

Background: The long-term survival of patients hospitalized with COVID-19 and the factors associated with poorer survival months after infection are not well understood. The aims of the present study were to analyze the overall mortality 10 months after admission. Methods: 762 patients with COVID-19 disease were included. Patients underwent a complete clinical evaluation, routine laboratory analysis and chest X-ray. Data collected included demographic and clinical data, such as vascular risk factors, tobacco or alcohol use, comorbidity, and institutionalization. Results: Ten-month mortality was 25.6%: 108 deaths occurred in-hospital, while 87 patients died after discharge. In-hospital mortality was independently related to NT-proBNP values > 503.5 pg/mL [OR = 4.67 (2.38–9.20)], urea > 37 mg/dL [3.21 (1.86–7.31)] and age older than 71 years [OR = 1.93 (1.05–3.54)]. NT-proBNP values > 503.5 pg/mL [OR = 5.00 (3.06–8.19)], urea > 37 mg/dL [3.51 (1.97–6.27)], cognitive impairment [OR = 1.96 (1.30–2.95), cancer [OR = 2.23 (1.36–3.68), and leukocytes > 6330/mm³ [OR = 1.64 (1.08–2.50)], were independently associated with long-term mortality. Conclusions: the risk of death remains high even months after COVID-19 infection. Overall mortality of COVID-19 patients during 10 months after hospital discharge is nearly as high as that observed during hospital admission. Comorbidities such as cancer or cognitive impairment, organ dysfunction and inflammatory reaction are independent prognostic markers of long-term mortality.