The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and ...potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.
Application of biological age as a measure of an individual´s health status offers new perspectives into extension of both lifespan and healthspan. While algorithms predicting mortality and most ...aging-related morbidities have been reported, the major shortcoming has been an inability to predict dementia. We present a community-based cohort study of 1930 participants with a mean age of 72 years and a follow-up period of over 7 years, using two variants of a phenotypic blood-based algorithm that either excludes (BioAge1) or includes (BioAge2) neurofilament light chain (NfL) as a neurodegenerative marker. BioAge1 and BioAge2 predict dementia equally well, as well as lifespan and healthspan. Each one-year increase in BioAge1/2 was associated with 11% elevated risk (HR 1.11; 95%CI 1.08-1.14) of mortality and 7% elevated risk (HR 1.07; 95%CI 1.05-1.09) of first morbidities. We additionally tested the association of microRNAs with age and identified 263 microRNAs significantly associated with biological and chronological age alike. Top differentially expressed microRNAs based on biological age had a higher significance level than those based on chronological age, suggesting that biological age captures aspects of aging signals at the epigenetic level. We conclude that accelerated biological age for a given age is a predictor of major age-related morbidity, including dementia, among healthy elderly.
Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new terminology updated from non-alcoholic fatty liver disease (NAFLD). In this study, we aim to estimate the global prevalence of ...MAFLD specifically in overweight and obese adults from the general population by performing a systematic review and meta-analysis through mining the existing epidemiological data on fatty liver disease.
We searched Medline, Embase, Web of Science, Cochrane and google scholar database from inception to November, 2020. DerSimonian-Laird random-effects model with Logit transformation was performed for data analysis. Sensitivity analysis and meta-regression were used to explore predictors of MAFLD prevalence in pooled statistics with high heterogeneity.
We identified 116 relevant studies comprised of 2,667,052 participants in general population with an estimated global MAFLD prevalence as 50.7% (95% CI 46.9-54.4) among overweight/obese adults regardless of diagnostic techniques. Ultrasound was the most commonly used diagnostic technique generating prevalence rate of 51.3% (95% CI, 49.1-53.4). Male (59.0%; 95% CI, 52.0-65.6) had a significantly higher MAFLD prevalence than female (47.5%; 95% CI, 40.7-54.5). Interestingly, MAFLD prevalence rates are comparable based on classical NAFLD and non-NAFLD studies in general population. The pooled estimate prevalence of comorbidities such as type 2 diabetes and metabolic syndrome was 19.7% (95% CI, 12.8-29.0) and 57.5% (95% CI, 49.9-64.8), respectively.
MAFLD has an astonishingly high prevalence rate in overweight and obese adults. This calls for attention and dedicated action from primary care physicians, specialists, health policy makers and the general public alike.
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Herpes simplex virus 1 (HSV1) is a neuroinvasive virus capable of entering the brain which makes it a candidate pathogen for increasing risk of dementia. Previous studies are inconsistent in their ...findings regarding the link between HSV1 and dementia, therefore, we investigated how HSV1 relates to cognitive decline and dementia risk using data from a population-based study. We measured HSV1 immunoglobulin (IgG) antibodies in serum collected between 2002 and 2005 from participants of the Rotterdam Study. We used linear regression to determine HSV1 in relation to change in cognitive performance during 2 consecutive examination rounds on average 6.5 years apart. Next, we determined the association of HSV1 with risk of dementia (until 2016) using a Cox regression model. We repeated analyses for Alzheimer's disease. All models were adjusted for age, sex, cardiovascular risk factors, and apolipoprotein E genotype. Of 1915 non-demented participants (mean age 71.3 years, 56.7% women), with an average follow-up time of 9.1 years, 244 participants developed dementia (of whom 203 Alzheimer's disease). HSV1 seropositivity was associated with decline in global cognition (mean difference of HSV1 seropositive vs seronegative per standard deviation decrease in global cognition - 0.16; 95% confidence interval (95%CI), - 0.26; - 0.07), as well as separate cognitive domains, namely memory, information processing, and executive function, but not motor function. Finally, HSV1 seropositivity was not associated with risk of dementia (adjusted hazard ratio 1.18, 95% CI 0.83; 1.68), similar for Alzheimer's disease. HSV1 is associated with cognitive decline but not with incident dementia in the general population. These data suggest HSV1 to be associated only with subtle cognitive disturbances but not with greater cognitive disorders that result in dementia.
Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of ...long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.
We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified ...21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10
), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10
) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r
≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r
| ≈ 0.1-0.3) and positive genetic correlations with physical activity (r
≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r
≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.
Each of the three satellites constituting the RADARSAT Constellation Mission (RCM) provides compact polarimetric synthetic aperture radar (CP SAR) data. The complex CP data have similar properties to ...the complex quad polarimetric (QP) data provided by prior RADARSAT missions. In this article, a land cover classification method using spatial information is designed based on the statistical characteristics of the complex CP and QP SAR data. First, the local spatial dependency among pixels is captured by superpixels. Second, a graph is constructed on the superpixels to model the global spatial dependency among superpixels. The land cover classification image with land cover type labels is then estimated by propagating labels from the few labeled superpixels to the unlabeled superpixels. Classification of two RCM complex CP and QP scenes demonstrates that the proposed method, with few labeled pixels, provides much higher classification accuracy than methods that do not exploit global spatial dependency.
The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. ...Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.
Genetic variants in the coding region could directly affect the structure and expression levels of genes and proteins. However, the importance of variants in the non-coding region, such as microRNAs ...(miRNAs), remain to be elucidated. Genetic variants in miRNA-related sequences could affect their biogenesis or functionality and ultimately affect disease risk. Yet, their implications and pleiotropic effects on many clinical conditions remain unknown.
Here, we utilised genotyping and hospital records data in the UK Biobank (N = 423,419) to investigate associations between 346 genetic variants in miRNA-related sequences and a wide range of clinical diagnoses through phenome-wide association studies. Further, we tested whether changes in blood miRNA expression levels could affect disease risk through colocalisation and Mendelian randomisation analysis.
We identified 122 associations for six variants in the seed region of miRNAs, nine variants in the mature region of miRNAs, and 27 variants in the precursor miRNAs. These included associations with hypertension, dyslipidaemia, immune-related disorders, and others. Nineteen miRNAs were associated with multiple diagnoses, with six of them associated with multiple disease categories. The strongest association was reported between rs4285314 in the precursor of miR-3135b and celiac disease risk (odds ratio (OR) per effect allele increase = 0.37, P = 1.8 × 10
). Colocalisation and Mendelian randomisation analysis highlighted potential causal role of miR-6891-3p in dyslipidaemia.
Our study demonstrates the pleiotropic effect of miRNAs and offers insights to their possible clinical importance.
Sleep and 24-h activity rhythm disturbances are associated with development of neurodegenerative diseases and related pathophysiological processes in the brain. We determined the cross-sectional ...relation of sleep and 24-h activity rhythm disturbances with plasma-based biomarkers that might signal neurodegenerative disease, in 4712 middle-aged and elderly non-demented persons. Sleep and activity rhythms were measured using the Pittsburgh Sleep Quality Index and actigraphy. Simoa assays were used to measure plasma levels of neurofilament light chain, and additionally β-amyloid 40, β-amyloid 42, and total-tau. We used linear regression, adjusting for relevant confounders, and corrected for multiple testing. We found no associations of self-rated sleep, actigraphy-estimated sleep and 24-h activity rhythms with neurofilament light chain after confounder adjustment and correction for multiple testing, except for a non-linear association of self-rated time in bed with neurofilament light chain (P = 2.5*10
). Similarly, we observed no significant associations with β-amyloid 40, β-amyloid 42, and total-tau after multiple testing correction. We conclude that sleep and 24-h activity rhythm disturbances were not consistently associated with neuronal damage as indicated by plasma neurofilament light chain in this population-based sample middle-aged and elderly non-demented persons. Further studies are needed to determine the associations of sleep and 24-h activity rhythm disturbances with NfL-related neuronal damage.
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