Gestational lipid profile as an early marker of metabolic syndrome in later life: a population-based prospective cohort study

Background: In pregnancy lipid levels increase with gestation resembling an atherogenic lipid profile. Currently it is unclear whether gestational lipid levels are associated with an adverse cardiovascular risk profile later in life. The aim of this study is to assess the association between gestational lipid levels and lipid levels and prevalence of the metabolic syndrome (MS) six years after pregnancy. Methods: In plasma of 3510 women from the Generation R Study; a prospective population-based cohort, we measured lipid levels (total cholesterol, triglycerides and high-density lipoprotein cholesterol [HDL-c]), and low-density lipoprotein cholesterol (LDL-c), remnant cholesterol and non-HDL-c were calculated in early pregnancy (median 13.2 weeks, 90% range [10.5 to 17.1]) and six years after pregnancy (median 6.5 years, 90% range [6.2 to 7.8]). MS was assessed six years after pregnancy according to the NCEP/ATP3 criteria. We also examined the influence of pregnancy complications on these associations. Results: Gestational lipid levels were positively associated with corresponding lipid levels six years after pregnancy, independent of pregnancy complications. Six years after pregnancy the prevalence of MS was 10.0%; the prevalence was higher for women with a previous placental syndrome (13.5%). Gestational triglycerides and remnant cholesterol in the highest quartile and HDL-c in the lowest quartile were associated with the highest risk for future MS, independent of smoking and body mass index. Conclusions: Gestational lipid levels provide an insight in the future cardiovascular risk profile of women in later life. Monitoring and lifestyle intervention could be indicated in women with an unfavorable gestational lipid profile to optimize timely cardiovascular risk prevention

Safety of intravenous ferric carboxymaltose versus oral iron in patients with nondialysis-dependent CKD: an analysis of the 1-year FIND-CKD trial.

Background: The evidence base regarding the safety of intravenous (IV) iron therapy in patients with chronic kidney disease (CKD) is incomplete and largely based on small studies of relatively short duration. Methods: FIND-CKD (ClinicalTrials.gov number NCT00994318) was a 1-year, open-label, multicenter, prospective study of patients with nondialysis-dependent CKD, anemia and iron deficiency randomized (1:1:2) to IV ferric carboxymaltose (FCM), targeting higher (400-600 µg/L) or lower (100-200 µg/L) ferritin, or oral iron. A post hoc analysis of adverse event rates per 100 patient-years was performed to assess the safety of FCM versus oral iron over an extended period. Results: The safety population included 616 patients. The incidence of one or more adverse events was 91.0, 100.0 and 105.0 per 100 patient-years in the high ferritin FCM, low ferritin FCM and oral iron groups, respectively. The incidence of adverse events with a suspected relation to study drug was 15.9, 17.8 and 36.7 per 100 patient-years in the three groups; for serious adverse events, the incidence was 28.2, 27.9 and 24.3 per 100 patient-years. The incidence of cardiac disorders and infections was similar between groups. At least one ferritin level ≥800 µg/L occurred in 26.6% of high ferritin FCM patients, with no associated increase in adverse events. No patient with ferritin ≥800 µg/L discontinued the study drug due to adverse events. Estimated glomerular filtration rate remained the stable in all groups. Conclusions: These results further support the conclusion that correction of iron deficiency anemia with IV FCM is safe in patients with nondialysis-dependent CKD

An artificial intelligence based app for skin cancer detection evaluated in a population based setting

Abstract Artificial intelligence (AI) based algorithms for classification of suspicious skin lesions have been implemented in mobile phone apps (mHealth), but their effect on healthcare systems is undocumented. In 2019, a large Dutch health insurance company offered 2.2 million adults free access to an mHealth app for skin cancer detection. To study the impact on dermatological healthcare consumption, we conducted a retrospective population-based pragmatic study. We matched 18,960 mHealth-users who completed at least one successful assessment with the app to 56,880 controls who did not use the app and calculated odds ratios (OR) to compare dermatological claims between both groups in the first year after granting free access. A short-term cost-effectiveness analysis was performed to determine the cost per additional detected (pre)malignancy. Here we report that mHealth-users had more claims for (pre)malignant skin lesions than controls (6.0% vs 4.6%, OR 1.3 (95% CI 1.2–1.4)) and also a more than threefold higher risk of claims for benign skin tumors and nevi (5.9% vs 1.7%, OR 3.7 (95% CI 3.4–4.1)). The costs of detecting one additional (pre)malignant skin lesion with the app compared to the current standard of care were €2567. Based on these results, AI in mHealth appears to have a positive impact on detecting more cutaneous (pre)malignancies, but this should be balanced against the for now stronger increase in care consumption for benign skin tumors and nevi

Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility studyResearch in context

Summary: Background: Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. Methods: We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Findings: Fifty patients were included with a median age of 52 years (IQR 33.5–60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4–6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p < 0.001). Interpretation: Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. Funding: SkinVision B.V

Maternal lipid levels in early pregnancy as a predictor of childhood lipid levels: a prospective cohort study

Background Maternal lipid levels in early pregnancy are associated with maternal health and foetal growth. It is however unclear if maternal lipids in early pregnancy can be used to predict childhood lipid levels. The aim of this study is to assess the association between maternal and offspring childhood lipid levels, and to investigate the influence of maternal BMI and diet on these associations. Methods This study included 2692 women participating in the Generation R study, an ongoing population-based prospective cohort study from early life onwards. Women with an expected delivery date between 2002 and 2006 living in Rotterdam, the Netherlands were included. Total cholesterol, triglycerides and high-density lipoprotein cholesterol (HDL-c) were measured in early pregnancy (median 13.2 weeks [90% range 10.6; 17.1]). Low-density lipoprotein cholesterol (LDL-c), remnant cholesterol and non-HDL-c were calculated. Corresponding lipid measurements were determined in 2692 children at the age of 6 (median 6.0 years [90% range 5.7; 7.5]) and 1673 children 10 years (median 9.7 years [90% range 9.5; 10.3]). Multivariate linear regression analysis was used to examine the association between maternal lipid levels in early pregnancy and the corresponding childhood lipid measurements at the ages of 6 and 10 years while adjusting for confounders. Results Maternal lipid levels in early pregnancy are positively associated with corresponding childhood lipid levels 6 and 10 years after pregnancy, independent of maternal body mass index and diet. Conclusions Maternal lipid levels in early pregnancy may provide an insight to the lipid profile of children years later. Gestational lipid levels may therefore be used as an early predictor of children’s long-term health. Monitoring of these gestational lipid levels may give a window-of-opportunity to start early interventions to decrease offspring’s lipid levels and possibly diminish their cardiovascular risk later in life. Future studies are warranted to investigate the genetic contribution on maternal lipid levels in pregnancy and lipid levels of their offspring years later