Background: Due to increased cardiometabolic risks and premature mortality in people with severe mental illness (SMI), monitoring cardiometabolic health is considered essential. We aimed to analyse screening rates and prevalences of cardiometabolic risks in routine mental healthcare and its associations with patient and disease characteristics.
Methods: We collected screening data in SMI from three mental healthcare institutions in the Netherlands, using most complete data on the five main metabolic syndrome (MetS) criteria (waist circumference, blood pressure, HDL-cholesterol, triglycerides, fasting blood glucose) within a 30-day timeframe in 2019/2020. We determined screened patients' cardiometabolic risks and analysed associations with patient and disease characteristics using multiple logistic regression.
Results: In 5037 patients, screening rates ranged from 28.8% (waist circumference) to 76.4% (fasting blood glucose) within 2019-2020, and 7.6% had a complete measurement of all five MetS criteria. Older patients, men and patients with psychotic disorders had higher odds of being screened. Without regarding medication use, risk prevalences ranged from 29.6% (fasting blood glucose) to 56.8% (blood pressure), and 48.6% had MetS. Gender and age were particularly associated with odds for individual risk factors. Cardiometabolic risk was present regardless of illness severity and did generally not differ substantially between diagnoses, in-/outpatients and institutions.
Conclusions: Despite increased urgency and guideline development for cardiometabolic health in SMI last decades, screening rates are still low, and the MetS prevalence across screened patients is almost twice that of the general population. More intensive implementation strategies are needed to translate policies into action to improve cardiometabolic health in SMI.
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http://dx.doi.org/10.1016/j.comppsych.2023.152406 | DOI Listing |
Nutr Metab Cardiovasc Dis
December 2024
Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain.
Backgrounds And Aim: To prospectively evaluate the associations between changes in (poly)phenol intake, body weight(BW), and physical activity(PA) with changes in an inflammatory score after 1-year.
Methods And Results: This is a prospective observational analysis involving 484 participants from the PREDIMED-Plus with available inflammatory measurements. (Poly)phenol intake was estimated using a validated semi-quantitative food frequency questionnaire and the Phenol-Explorer database.
J Prev Alzheimers Dis
January 2025
School of Psychology, University of New South Wales, Sydney, NSW 2057, Australia; Neuroscience Research Australia, Margarete Ainsworth Building, 139 Barker St, Randwick NSW 2031, Australia. Electronic address:
Background: A brain healthy lifestyle, consisting of good cardiometabolic health and being cognitively and socially active in midlife, is associated with a lower risk of cognitive decline years later. However, it is unclear whether lifestyle changes over time also affect the risk for mild cognitive impairment (MCI)/dementia, and rate of cognitive decline.
Objectives: To investigate if lifestyle changes over time are associated with incident MCI/dementia risk and rate of cognitive decline.
J Prev Alzheimers Dis
January 2025
Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China. Electronic address:
Background: While optimal cardiovascular health (CVH) has been linked to a lower risk of dementia, few studies considered individuals' genetic background. We aimed to examine the interaction between CVH and genetic predisposition on dementia risk among individuals with atherosclerotic cardiovascular disease (ASCVD).
Methods: We included 30,818 ASCVD patients from the UK Biobank.
Clin Nutr ESPEN
January 2025
Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6DZ, UK; Institute for Food, Nutrition, and Health (IFNH), University of Reading, Reading, RG6 6AP, UK. Electronic address:
Background & Aims: Cardiometabolic traits are complex interrelated traits that result from a combination of genetic and lifestyle factors. This study aimed to assess the interaction between genetic variants and dietary macronutrient intake on cardiometabolic traits [body mass index, waist circumference, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, triacylglycerol, systolic blood pressure, diastolic blood pressure, fasting serum glucose, fasting serum insulin, and glycated haemoglobin].
Methods: This cross-sectional study consisted of 468 urban young adults aged 20 ± 1 years, and it was conducted as part of the Study of Obesity, Nutrition, Genes and Social factors (SONGS) project, a sub-study of the Young Lives study.
Comput Biol Med
January 2025
Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6DZ, UK; Institute for Food, Nutrition and Health (IFNH), University of Reading, Reading, RG6 6AH, UK. Electronic address:
Background: Machine learning (ML) integration of clinical, metabolite, and genetic data reveals variable results in predicting cardiometabolic health (CMH) outcomes. Therefore, we aim to (1) evaluate whether a multi-modal approach incorporating all three data types using ML algorithms can improve CMH outcome prediction compared to single-modal or paired-modal models, and (2) compare the methodologies used in existing prediction models.
Methods: We systematically searched five databases from 1998 to 2024 for ML predictive modelling studies using the multi-modal approach for CMH outcomes.
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