Fixed air quality stations have limitations when used to assess people's real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification.

Deep neural networks can extract clinical information, such as diabetic retinopathy status and individual characteristics (e.g. age and sex), from retinal images. Here, we report the first study to train deep learning models with retinal images from 3,000 Qatari citizens participating in the Qatar Biobank study. We investigated whether fundus images can predict cardiometabolic risk factors, such as age, sex, blood pressure, smoking status, glycaemic status, total lipid panel, sex steroid hormones and bioimpedance measurements. Additionally, the role of age and sex as mediating factors when predicting cardiometabolic risk factors from fundus images was studied. Predictions at person-level were made by combining information of an optic disc centred and a macula centred image of both eyes with deep learning models using the MobileNet-V2 architecture. An accurate prediction was obtained for age (mean absolute error (MAE): 2.78 years) and sex (area under the curve: 0.97), while an acceptable performance was achieved for systolic blood pressure (MAE: 8.96 mmHg), diastolic blood pressure (MAE: 6.84 mmHg), Haemoglobin A1c (MAE: 0.61%), relative fat mass (MAE: 5.68 units) and testosterone (MAE: 3.76 nmol/L). We discovered that age and sex were mediating factors when predicting cardiometabolic risk factors from fundus images. We have found that deep learning models indirectly predict sex when trained for testosterone. For blood pressure, Haemoglobin A1c and relative fat mass an influence of age and sex was observed. However, achieved performance cannot be fully explained by the influence of age and sex. In conclusion we confirm that age and sex can be predicted reliably from a fundus image and that unique information is stored in the retina that relates to blood pressure, Haemoglobin A1c and relative fat mass. Future research should focus on stratification when predicting person characteristics from a fundus image.

Adequate fruit and vegetable (F and V) intake, as recommended by the World Health Organization (over 400 g/day), is linked to reduced chronic disease risk. However, human intervention trials, especially with whole F and V and in complex combinations, are lacking. The MiBlend Study explored the effects of various phytochemical-rich F and V combinations on chronic disease risk markers, phytochemical absorption, and gene expression in blood. This randomized cross-over study involved participants consuming two of seven different F and V blends for 2 weeks (450 g/day), following a 2-week low F and V intake period (50 g/day). Each blend represented major phytochemical classes (flavonoids, anthocyanins, carotenoids, and glucosinolates) or combinations thereof. Markers of chronic disease risk, including DNA damage, oxidative stress, and retinal microvasculature, were measured. Increasing F and V intake significantly improved plasma antioxidant capacity, DNA damage protection, and retinal arteriolar dilation. Flavonoid-rich, carotenoid-rich, and complex blends notably reduced DNA damage susceptibility. Anthocyanin-rich and carotenoid-rich interventions were most effective in boosting antioxidant capacity, while blends high in flavonoids, especially combined with anthocyanins, significantly improved retinal microvasculature. Gene expression analysis revealed changes in DNA repair, signal transduction, and transcription processes, indicating mechanisms for these health benefits. The study suggests specific F and V blends can provide targeted health improvements, emphasizing the importance of both overall F and V intake and the specific phytochemical composition for personalized preventive strategies.

Aim: To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis. Methods: The algorithm’s threshold value was fixed at the 90% sensitivity operating point on the receiver operating curve to perform the disease classification. Diagnostic performance was appraised on a private test set and publicly available datasets. Stratification analysis was executed on the private test set considering age, ethnicity, sex, insulin dependency, year of examination, camera type, image quality, and dilatation status. Results: The software displayed an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on the private test set. The specificity and sensitivity for combined DR and DME predictions were 94.24 and 90.91%, respectively. The AUC ranged from 96.91 to 97.99% on the publicly available datasets for DR. AUC values were above 95% in all subgroups, with lower predictive values found for individuals above the age of 65 (82.51% sensitivity) and Caucasians (84.03% sensitivity). Conclusion: We report good overall performance of the MONA.health screening software for DR and DME. The software performance remains stable with no significant deterioration of the deep learning models in any studied strata.

Background: Low gestational vitamin D levels may increase offspring risk of cardiovascular disease from an early age. Studies investigating the impact on offspring macrovascular function have been inconsistent. Few included pulse wave velocity as an arterial stiffness indicator, and none included measures of microvascularization as an early marker of cardiovascular health. This study explored the association between gestational vitamin D levels and macro‐ and microvascular health across early adolescence. Methods and Results: We analyzed data from 430 mother–child pairs from a Spanish birth cohort. 25‐hydroxyvitamin D ₃ (vitamin D ₃ ) levels were measured in serum at 13 weeks of pregnancy. At 11 and 15 years we assessed macrovascular parameters, including systolic and diastolic blood pressure (mm Hg) and pulse wave velocity (m/s), and microvascular parameters (central retinal artery/vein equivalent (μm)). We used continuous (in ng/mL) and categorical (deficient 20 ng/mL) deseasonalized 25(OH)D ₃ levels as exposure. Mixed effect and linear regression models were conducted. During their pregnancies, nearly 23% of the mothers had deficient vitamin D ₃ levels. We did not find statistically significant associations between pregnancy vitamin D ₃ levels and macro‐ and microvascular function markers across adolescence. However, subjects exposed to deficient vitamin D ₃ levels showed a nonstatistically significant decrease in pulse wave velocity ( β =−0.09 [95% CI, −0.19 to 0.01]) compared with those exposed to adequate levels. There was no evidence of a sex interaction. Conclusions: Our findings show little evidence to support associations between low vitamin D levels during pregnancy and macro‐ or microvascular health parameters through early adolescence.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.