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Objective This study aimed to describe longitudinal trajectories of Total Weight Loss (%TWL), and mental and physical health related quality of life (HRQOL), as well as to identify preoperative psychological predictors of these trajectories.Methods A prospective observational study including Dutch patients treated with metabolic and bariatric surgery (n = 420, age 44.8 ± 10.3 years, 78.6% females) was performed. Trajectories of %TWL and HRQOL from screening to 1-, 2-, and 3-years post-surgery were described using growth mixture modelling. Multivariable and lasso regression models were used to identify predictors.Results Three trajectories described %TWL, varying in the degree of first-year weight loss. No pre-surgical psychological factors were associated with %TWL trajectories. We identified four physical and five mental HRQOL trajectories. Approximately 25-30% of patients exhibited patterns of initial improvements followed by decline, or persistently low levels of HRQOL. Higher depressive symptoms were associated with these unfavourable physical HRQOL trajectories (OR 1.20, 95%CI 1.04-1.39), adjusted for confounders. Unfavourable mental HRQOL trajectories were predicted by depressive and anxiety symptoms, neuroticism, insecure attachment, and maladaptive coping. In contrast, self-esteem, extraversion, and conscientiousness were associated with favourable mental HRQOL trajectories.Discussion Psychological factors did not predict weight loss, but they significantly impacted patient's HRQOL after metabolic and bariatric surgery. A subgroup with unsuccessful HRQOL after surgery was identified, who would benefit from tailored preoperative counselling to optimize surgery outcomes. Metabolic and bariatric surgery may not be universally beneficial for all patients, challenging the conventional approach to surgical interventions for severe obesity and advocating for a more nuanced, individualized assessment of potential candidates.

Background The current study aims to enhance insight into the heterogeneity of long COVID by identifying symptom clusters and associated socio-demographic and health determinants. Methods A total of 458 participants (Mage 36.0 ± 11.9; 46.5% male) with persistent symptoms after COVID-19 completed an online self-report questionnaire including a 114-item symptom list. First, a k-means clustering analysis was performed to investigate overall clustering patterns and identify symptoms that provided meaningful distinctions between clusters. Next, a step-three latent class analysis (LCA) was performed based on these distinctive symptoms to analyze person-centered clusters. Finally, multinominal logistic models were used to identify determinants associated with the symptom clusters. Results From a 5-cluster solution obtained from k-means clustering, 30 distinctive symptoms were selected. Using LCA, six symptom classes were identified: moderate (20.7%) and high (20.7%) inflammatory symptoms, moderate malaise-neurocognitive symptoms (18.3%), high malaise-neurocognitive-psychosocial symptoms (17.0%), low-overall symptoms (13.3%) and high overall symptoms (9.8%). Sex, age, employment, COVID-19 suspicion, COVID-19 severity, number of acute COVID-19 symptoms, long COVID symptom duration, long COVID diagnosis, and impact of long COVID were associated with the different symptom clusters. Conclusions The current study’s findings characterize the heterogeneity in long COVID symptoms and underscore the importance of identifying determinants of different symptom clusters.

Objectives Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom clustering. Methods A total of 3,022 participants of a panel representative of the Dutch population completed an online survey about long COVID symptoms. Data was merged into 2018/2019 panel data covering sociodemographic, medical, and psychosocial predictors. A total of 415 participants were classified as having long COVID. K-means clustering was used to identify patient clusters. Multivariate and lasso regression was used to identify relevant predictors compared to a COVID-19 positive control group. Results Predictors of long-term COVID included older age, Western ethnicity, BMI, chronic disease, COVID-19 reinfections, severity, and symptoms, lower self-esteem, and higher positive affect (AUC = 0.79, 95%CI 0.73-0.86). Four clusters were identified: a low and a high symptom severity cluster, a smell-taste and respiratory symptoms cluster, and a neuro-cognitive, psychosocial, and inflammatory symptom cluster. Predictors for the different clusters included regular health complaints, healthcare use, fear of COVID-19, anxiety, depressive symptoms, and neuroticism. Conclusions A combination of sociodemographic, medical, and psychosocial factors predicted long COVID. Heterogenous symptom clusters suggest that there are different phenotypes of long COVID-19 presentation.

Background Evidence on associations between dairy consumption and incident prediabetes is inconsistent. One potential explanation for heterogeneity is that health behavior and food intake covary with the consumption of various high-fat and low-fat dairy types. Objective The objective was to investigate the associations of total dairy and dairy types with incident prediabetes and to assess how dairy intake is linked with metabolic risk factors, lifestyle behaviors, and foods, as potential explanations for these associations. Methods Overall, 74,132 participants from the prospective population-based Lifelines study were included (mean age, 45.5 ± 12.3 y; 59.7% female). Baseline dairy intake was measured using a validated food frequency questionnaire. Prediabetes at follow-up was defined based on the World Health Organization/International Expert Committee criteria as fasting plasma glucose of 110–125 mg/dL or glycated hemoglobin concentrations of 6.0%–6.5%. Associations were analyzed using Poisson regression models adjusted for social demographics, lifestyle behaviors, family history of diabetes, and food group intake. Interconnections were assessed with mixed graphical model networks. Results At a mean follow-up of 4.1 ± 1.1 y, 2746 participants developed prediabetes (3.7%). In regression analyses, neutral associations were found for most dairy types. Intake of plain milk and low-fat milk were associated with a higher risk of prediabetes in the top compared with bottom quartiles (relative risk [RR]: 1.17; 95% confidence interval [CI]: 1.05, 1.30; P-trend = 0.04 and RR: 1.18; 95% CI: 1.06, 1.31; P-trend =0.01). Strong but nonsignificant effect estimates for high-fat yogurt in relation to prediabetes were found (RR servings/day: 0.80; 95% CI: 0.64, 1.01). The network analysis showed that low-fat milk clustered with energy-dense foods, including bread, meat, and high-fat cheese, whereas high-fat yogurt had no clear link