•Predictors of long-term COVID-19 were explored in a representative sample of Dutch adults.•Pre-pandemic demographic, medical, and psychological data were available.•Predictors of long-term COVID ...included age, BMI, COVID-19 severity, and positive affect.•Four distinct long COVID symptom clusters were found, each with unique predictors.•Collectively, results point to heterogeneous phenotypes of long COVID.
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.
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.
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.
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.
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.
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.
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.
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.
•Pre-surgical psychological factors did not predict variation in post-operative weight-loss.•Depressive symptoms were related to low or decreasing post-operative physical HRQOL.•Multiple internalizing symptoms were associated with worse mental HRQOL.•Tailored preoperative counselling is essential to optimize surgery outcomes.•Bariatric surgery may not be universally beneficial for all patients.
Purpose
Our aim was to investigate prospective associations of consumption of total dairy and dairy types with incident prediabetes in a Dutch population-based study.
Methods
Two enrolment waves of ...the Hoorn Studies were harmonized, resulting in an analytic sample of 2262 participants without (pre-) diabetes at enrolment (mean age 56 ± 7.3 years; 50% male). Baseline dietary intake was assessed by validated food frequency questionnaires. Relative risks (RRs) were calculated between dairy, fermented dairy, milk, yogurt (all total/high/low fat), cream and ice cream and prediabetes. Additionally, substituting one serving/day of dairy types associated with prediabetes with alternative dairy types was analysed.
Results
During a mean 6.4 ± 0.7 years of follow-up, 810 participants (35.9%) developed prediabetes. High fat fermented dairy, cheese and high fat cheese were associated with a 17% (RR 0.83, 95% CI 0.69–0.99,
p
trend
= 0.04), 14% (RR 0.86, 95% CI 0.73–1.02,
p
trend
= 0.04) and 21% (RR 0.79, 95% CI 0.66–0.94
, p
trend
= 0.01) lower risk of incident prediabetes, respectively, in top compared to bottom quartiles, after adjustment for confounders. High fat cheese consumption was continuously associated with lower prediabetes risk (RR
servings/day
0.94, 95% CI 0.88–1.00,
p
= 0.04). Total dairy and other dairy types were not associated with prediabetes risk in adjusted models, irrespective of fat content (RR ~ 1). Replacing high fat cheese with alternative dairy types was not associated with prediabetes risk.
Conclusion
The highest intake of high fat fermented dairy, cheese and high fat cheese were associated with a lower risk of prediabetes, whereas other dairy types were not associated. Cheese seems to be inversely associated with type 2 diabetes risk, despite high levels of saturated fatty acids and sodium.
Evidence suggests neutral or moderately beneficial effects of dairy intake on type 2 diabetes mellitus risk. Nevertheless, evidence on associations with early phases of type 2 diabetes remains ...inconsistent. We aimed to examine associations between dairy-type intake with prediabetes risk and longitudinal insulin resistance. The analytic sample consisted of 6770 participants (aged 62 ± 4 years, 59% female) free of (pre-)diabetes at baseline from the prospective population-based Rotterdam Study. Dairy intake was measured at baseline using food frequency questionnaires. Data on prediabetes (fasting blood glucose 6.1-6.9 mmol/L or non-fasting 7.7-11.1 mmol/L) and the longitudinal homeostatic model assessment of insulin resistance (HOMA-IR) were available from 1993-2015. Associations with these outcomes were analyzed with dairy intake in quartiles (Q4 vs. Q1) and continuous using multivariable Cox proportional hazard models and linear mixed models. During a mean follow-up of 11.3 ± 4.8 years, 1139 incident prediabetes cases were documented (18.8%). In models adjusting for sociodemographic, lifestyle and dietary factors, a higher intake of high-fat yogurt was associated with lower prediabetes risk (HR
0.70, 95% CI 0.54-0.91 and HR
0.67, 0.51-0.89). In addition, a higher intake of high-fat milk was associated with lower prediabetes risk (HR
0.81, 0.67-0.97, HR
0.88, 0.79-0.99). Associations were found for low-fat dairy, low-fat milk and total cheese with a higher prediabetes risk (HR
ranging from 1.05-1.07, not significant in quartiles). Associations with longitudinal HOMA-IR were similar to prediabetes for high-fat yogurt, low-fat dairy and low-fat milk. Fermented dairy, low-fat yogurt, high-fat cheese, cream and ice cream were not associated with the outcomes. In conclusion, a higher intake of high-fat yogurt was associated with a lower prediabetes risk and lower longitudinal insulin resistance. Additionally, high-fat milk was associated with a lower prediabetes risk. Some low-fat dairy types were inconsistently associated with these outcomes. Studies are needed to confirm associations and to examine the influence of confounding by population characteristics.
The COVID-19 pandemic has substantial implications for physical and mental wellbeing. This study investigated changes, over time, in lifestyle behaviors and perceived stress during the initial phase ...of the pandemic and associations with COVID-19 symptoms, in the Dutch general population. An online longitudinal survey study was performed with pre-lockdown measurements in February, and subsequently in April and June 2020 (n = 259, mean age 59 ± 14 years, 59% women). Self-report questionnaires were used to assess weight, diet quality, physical activity, alcohol intake, and smoking. Perceived stress was measured using the validated perceived stress scale (PSS-10). The presence of COVID-19 symptoms (yes/no) was defined as fever, or >3 of the following symptoms: weakness/tiredness, muscle ache, dry cough, loss of smell/taste, and breathing difficulties. Data were analyzed using linear mixed models, adjusted for age, sex, educational level, marital status and (change in) employment status. Minimal increases over time were observed in alcohol intake (0.6 ± 0.7 to 0.7 ± 1.1 glasses/day, p = 0.001) and smoking (9.5 ± 8.7 to 10.9 ± 9.4 cigarettes/day among 10% smokers, p = 0.03), but other lifestyle behaviors remained stable. In April 2020, 15% reported COVID-19-related symptoms, and in June 2020, this was 10%. The presence of COVID-19 symptoms was associated with increased perceived stress (pinteraction = 0.003) and increased alcohol consumption (pinteraction = 0.03) over time. In conclusion, in this prospective study, COVID-19 symptoms were associated with increases in perceived stress and alcohol consumption. Future research on biopsychosocial determinants and underlying mechanisms of lifestyle changes, as a response to the COVID-19 pandemic, is needed.
The current study aims to enhance insight into the heterogeneity of long COVID by identifying symptom clusters and associated socio-demographic and health determinants.
A total of 458 participants (M
...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.
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.
The current study's findings characterize the heterogeneity in long COVID symptoms and underscore the importance of identifying determinants of different symptom clusters.
ObjectivesContinuing high STI positivity among men who have sex with men (MSM) attending centres for sexual health (CSH) indicates that high-risk behaviour is ongoing. The objective of this study was ...to gain a better insight into risk behaviours among MSM attending CSH and to explore STI and HIV positivity by subgroups.MethodsWe used national data routinely collected during CSH consultations for this study. From September to December 2017, questions on group sex, substance use and sex with HIV-positive partners were asked at each CSH consultation. We analysed latent classes of client-related factors and sexual risk behaviour among MSM attending CSH in this period. We examined STI positivity and prevalence ratios by latent classes.ResultsA total of six classes were identified in order of increasing risk: ‘overall low-risk behaviour’ (n=2974; 22.0%), ‘Western origin and multiple sex partners’ (MSP) (n=4182; 30.9%), ‘Non-Western origin and MSP’ (n=2496; 18.5%), ‘living with HIV’ (n=827; 6.1%), ‘group sex and HIV-positive partners’ (n=1798; 13.3%) and ‘group sex and chemsex’ (n=1239; 9.2%). The any STI positivity ranged from 14.0% in the overall low-risk behaviour class to 35.5% in the group sex and chemsex class. HIV positivity did not differ significantly between classes. The Western origin and MSP class was largest and accounted for the majority of STI and HIV infections.ConclusionsAlthough STI positivity increased with increased risky behaviours, considerable STI positivity was found in all six latent classes. Comparable HIV positivity between classes indicates risk reduction strategies among subgroups engaged in risky behaviours. The differences in risk behaviour and STI positivity require preventive strategies tailored to each subgroup.
Background:
Surveillance of recent HIV infections (RHI) using an avidity assay has been implemented at Dutch sexual health centres (SHC) since 2014, but data on RHI diagnosed at other test locations ...is lacking.
Setting:
Implementation of the avidity assay in HIV treatment clinics for the purpose of studying RHI among HIV patients tested at different test locations.
Methods:
We retrospectively tested leftover specimens from newly diagnosed HIV patients in care in 2013–2015 in Amsterdam. Avidity Index (AI) values ≤0.80 indicated recent infection (acquired ≤6 months prior to diagnosis), and AI > 0.80 indicated established infection (acquired >6 months prior to diagnosis). An algorithm for RHI was applied to correct for false recency. Recency based on this algorithm was compared with recency based on epidemiological data only. Multivariable logistic regression analysis was used to identify factors associated with RHI among men who have sex with men (MSM).
Results:
We tested 447 specimens with avidity; 72% from MSM. Proportions of RHI were 20% among MSM and 10% among heterosexuals. SHC showed highest proportions of RHI (27%), followed by GPs (15%), hospitals (5%), and other/unknown locations (11%) (
p
< 0.001). Test location was the only factor associated with RHI among MSM. A higher proportion of RHI was found based on epidemiological data compared to avidity testing combined with the RHI algorithm.
Conclusion:
SHC identify more RHI infections compared to other test locations, as they serve high-risk populations and offer frequent HIV testing. Using avidity-testing for surveillance purposes may help targeting prevention programs, but the assay lacks robustness and its added value may decline with improved, repeat HIV testing and data collection.
Investigating modifiable risk factors for the early stages of the development of type 2 diabetes is essential for effective prevention. Some studies show protective associations between dairy and ...prediabetes; however, associations are heterogeneous by the type and fat content of dairy foods.
To examine the relationship between the consumption of dairy, including different types of dairy products and risk of prediabetes.
The study included 4891 participants with normal glucose tolerance (aged 49.0 ± 12.3 y, 57% female) of the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study, a longitudinal population-based study. Dairy intake was measured at baseline using a food frequency questionnaire. Prediabetes at the 5-y and 12-y follow-ups was defined according to the WHO criteria as fasting plasma glucose levels of 110–125 mg/dL or 2-h plasma glucose levels of 140–199 mg/dL. Associations were analyzed using Poisson regression, adjusted for social demographics, lifestyle behaviors, a family history of diabetes, and food group intake.
In total, 765 (15.6%) incident cases of prediabetes were observed. The mean intake of dairy foods was 2.4 ± 1.2 servings/d, mostly consisting of low-fat milk (0.70 ± 0.78 servings/d) and high-fat milk (0.47 ± 0.72 servings/d). A higher intake of high-fat dairy (RRservings/d: 0.92; 95% CI: 0.85, 1.00), high-fat milk (0.89; 95% CI: 0.80, 0.99), and total cheese (0.74; 95% CI: 0.56, 0.96) was associated with a lower risk of prediabetes. Low-fat milk intake was associated nonlinearly with prediabetes risk. Low-fat dairy foods, total milk, yogurt, low-fat cheese, and ice cream were not associated with prediabetes risk.
In this large Australian cohort, protective associations were found for high-fat dairy types, whereas neutral associations were seen for low-fat dairy types. Studies with more detail on sugar content of types of dairy foods and products eaten with dairy foods (e.g., cereals or jam), and studies into potential causal mechanisms of the health effects of dairy intake are required.
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