Sarcopenic obesity (SO) is defined as the combination of excess fat mass (obesity) and low skeletal muscle mass and function (sarcopenia). The identification and classification of factors related to SO would favor better prevention and diagnosis. The present article aimed to (i) define a list of factors related with SO based on literature analysis, (ii) identify clinical conditions linked with SO development from literature search and (iii) evaluate their relevance and the potential research gaps by consulting an expert panel. From 4746 articles screened, 240 articles were selected for extraction of the factors associated with SO. Factors were classified according to their frequency in the literature. Clinical conditions were also recorded. Then, they were evaluated by a panel of expert for evaluation of their relevance in SO development. Experts also suggested additional factors. Thirty-nine unique factors were extracted from the papers and additional eleven factors suggested by a panel of experts in the SO field. The frequency in the literature showed insulin resistance, dyslipidemia, lack of exercise training, inflammation and hypertension as the most frequent factors associated with SO whereas experts ranked low spontaneous physical activity, protein and energy intakes, low exercise training and aging as the most important. Although literature and expert panel presented some differences, this first list of associated factors could help to identify patients at risk of SO. Further work is needed to confirm the contribution of factors associated with SO among the population overtime or in randomized controlled trials to demonstrate causality.

The COVID-19 pandemic and subsequent lockdowns modified work environments, lifestyles, and food consumption. Eating habits and mood changes in a French population during the first lockdown were examined using an online self-reported questionnaire with REDCap software through the COVISTRESS.ORG website. In 671 French participants, the main changes during lockdown were increased stress levels (64 [23; 86] vs. 3 [0; 18]) and sedentary behavior (7 [4; 9] vs. 5 [3; 8] hours per day), a deterioration in sleep quality (50 [27; 83] vs. 70 [48; 94]) and mood (50 [30; 76] vs. 78 [50; 92]), and less physical activity (2.0 [0.5; 5.0] vs. 3.5 [2.0; 6.0]). Mood was modified, with more anger (56 [39; 76] vs. 31 [16; 50]), more sadness (50 [34; 72] vs. 28 [16; 50]), more agitation (50 [25; 66] vs. 43 [20; 50]), and more boredom (32 [7; 60] vs. 14 [3; 29]). A total of 25% of the participants increased their consumption of alcoholic beverages, 29% their consumption of sugary foods, and 26% their consumption of cocktail snacks. A multiple-correspondence analysis highlights four different profiles according to changes in eating habits, food consumption, lifestyle, and mood. In conclusion, eating habits and lifestyle changes during lockdown periods should be carefully monitored to promote healthy behaviors.

Independently of absolute BMI values, the amount, onset date, and duration of early body weight gain may influence cardio-metabolic health later in adulthood. Values of cardiac and metabolic variables from a cohort study of morbidly obese patients were retrospectively analyzed to study the association between early weight history and metabolic syndrome (MetS) occurrence in adults. Of 950 patients with severe morbid obesity (age 44.3 ± 13.8 y, BMI 42.5 ± 7.0 kg/m²), 31.4% had started excess weight gain in childhood (CH), 19.9% in adolescence (ADO), and 48.7% in adulthood (AD). Despite different BMI values, MetS prevalence (57.8%) was not significantly different in the three groups (54.4% CH vs. 57.7% ADO vs. 59.8% AD, p = 0.59). The overweight onset period was not significantly associated with the development of MetS in adults (ADO: OR = 1.14 [0.69–1.92], p = 0.60; AD: OR = 0.99 [0.62–1.56], p = 0.95) despite a higher BMI in the early obesity onset group. Weight gain of more than 50% after age 18 years significantly increased the risk of MetS (OR = 1.75 [1.07–2.88], p = 0.026). In addition to crude BMI values, analysis of body mass trajectories is a relevant clinical tool in the assessment of metabolic risk, suggesting that the magnitude of weight gain may be more important for metabolic syndrome progression than the period of obesity onset.

Alexithymia is a personality trait characterized by difficulties in identifying and describing emotions. It is associated with impaired emotional regulation, maladaptive coping strategies, and various psychiatric and metabolic conditions. In individuals with obesity, alexithymia may contribute to binge eating disorder (BED), but available evidence remains limited and inconsistent. The objective of this retrospective cross-sectional study was to examine the association between alexithymia and BED in patients with obesity, taking into account co-variables influencing these two components. A sample of 1.456 patients with obesity (148 with BED and 1.308 without BED) was evaluated using: Toronto Alexithymia Scale (TAS-20), Beck Depression Inventory II score (BDI II), Impulsive Behavior Scale (UPPS), Short Physical Performance Battery (SPPB), and biological analyses were performed. Hypotheses were tested using both univariate and multivariate modeling analyses. Alexithymia scores were higher in BED patients (61.0 ± 12.7 vs 58.5 ± 11.8, p = 0.001), and a greater proportion of them had a positive alexithymia score (55.4% vs 42.9%, p = 0.004) in univariate analysis. Mean depression scores were also higher (23.7 ± 11.9 vs 15.8 ± 10.7, p < 0.001), with an increased prevalence of severe depression (39.2% vs 15.8%, p < 0.001). However, the multivariate model analysis on BED did not confirm a significant link with alexithymia (OR:1.45, 95% CI:0.88 to 2.41, p = 0.149), but did with depression (OR:1.04, 95% CI:1.02 to 1.06, p = 0.001). These results suggest that the apparent association between alexithymia and BED in individuals with obesity is substantially attenuated after adjustment for other psychological factors, particularly depressive symptoms. These data underscore the importance of a multidimensional approach to understanding and treating these conditions, emphasizing the necessity of primary prevention and early screening for emotional disorders. However, given the retrospective cross-sectional and single-center design, causal inferences cannot be drawn.

To analyze the association between clinical factors and the presence of albuminuria in non-diabetic patients with obesity. retrospective, single-center observational study in a population of adult patients with severe obesity but without diabetes. Two groups were defined: patients with albuminuria ≥30 mg/24h and those with albuminuria <30 mg/24h. Among the 254 patients (mean ± standard deviation, age 43.5 ± 13.1 years; BMI 43.5 ± 6.0 kg/m²), 16.9% of patients had albuminuria ≥30 mg/24h (95% confidence interval: 12.5 to 22.1). Male gender (48.8% vs. 25.6%, p = 0.002), weight (130.9 ± 25.4 kg vs. 118.7 ± 21.7 kg, p = 0.005), cardiovascular risk (Framingham risk: 2.46 [0.61; 6.68] vs. 0.92 [0.20; 3.09], p = 0.007), albumin-to-creatinine ratio (5.64 [2.46; 8.78] vs. 0.84 [0.56; 1.15], p < 0.001) and plasma triglycerides (1.62 g/l [1.16; 2.28] vs. 1.30 g/l [1.04; 1.85], p = 0.049) appeared significantly associated with the presence of albuminuria. The metabolic syndrome and the metabolic syndrome severity score were not significantly associated with the presence of albuminuria. In the multivariable analysis, high triglyceridemia was mostly associated with a risk of albuminuria (odds ratio: 3.59, 95% confidence interval: 1.09 to 11.89, p = 0.04). In patients with severe obesity, early signs of renal failure should be systematically detected regardless of metabolic status. Nevertheless, both metabolic (hypertriglyceridemia) and non-metabolic factors (male gender and weight) were associated with renal impairment. The limited power of this study to fully assess the role of metabolic syndrome should be acknowledged.