Adverse maternofoetal outcomes associated with ionised calcaemia, total calcaemia, albuminaemia, and calcium supplementation in pregnancy: Analysis from a resource-limited setting

Efficacy of a Personalized mHealth App in Improving Micronutrient Supplement Use Among Pregnant Women in Karachi, Pakistan: Parallel-Group Randomized Controlled Trial

BACKGROUND

Micronutrient deficiencies in folate, ferritin, calcium, and vitamin D are common during pregnancy in low- and middle-income countries, often due to inadequate diets. Micronutrient supplementation can address this need, whereas innovative awareness strategies in antenatal practices could enhance supplement use compliance.

OBJECTIVE

We evaluated the efficacy of a personalized mobile health (mHealth) intervention, hypothesizing a 30% improvement in supplement use in the intervention group compared to a conventional face-to-face counseling group.

METHODS

In an unblinded randomized controlled trial, we enrolled 306 first-trimester pregnant women from Aga Khan University Hospital between January 2020 and September 2021 who owned smartphones with internet connection. Women on regular medications or with dietary restrictions or critical illnesses were excluded. The intervention group received personalized micronutrient supplement use coaching through an mHealth app (PurUmeed Aaghaz) as thrice-a-week push messages and tailored recommendations over a 24-week period. The comparison group received standard face-to-face counseling at 6, 12, 18, and 24 weeks after enrollment. Baseline sociodemographic, obstetrics, anthropometric, dietary, and lifestyle data were collected through face-to-face interviews. At each follow-up, participants reported their weekly use of folic acid, iron, calcium, and vitamin D supplements, scored as 0 (daily), 1.5 (4-6 times weekly), and 3 (≤3 times weekly). Scores were summed to calculate the cumulative supplement use score (CSUS; 0-12), with higher scores indicating greater inadequacy. Every fourth woman was invited for biochemical micronutrient assessment. Data were analyzed using Stata (version 14), with random-effects linear and logistic panel regression to compare CSUS and supplement use between the 2 groups from baseline to endline.

RESULTS

Of 153 participants per group, 107 (69.9%) in the intervention and 125 (81.7%) in the nonintervention group completed the study. After 24 weeks, the intervention group showed a greater but insignificant reduction in mean CSUS compared to the nonintervention group (β=-.27, 95% CI -0.65 to 0.12; P=.17). Daily supplement use improved by 20% versus 22.4% for folic acid, 11.2 times versus 2.1 times for iron, 1.2 times versus 14.2 times for calcium, and 3 times versus 1.3 times for vitamin D in the intervention versus nonintervention group, respectively. Multivariable analysis showed higher, though insignificant, odds of sufficient folic acid (adjusted odds ratio [aOR] 1.26, 95% CI 0.68-2.36; P=.46) and iron (aOR 1.31, 95% CI 0.95-1.81; P=.10) use in the intervention group, whereas vitamin D use was significantly higher (aOR 1.88, 95% CI 1.43-2.47; P<.001). Calcium intake improved in the nonintervention group (aOR 0.59, 95% CI 0.44-0.79; P<.001). Anemia decreased in the intervention group, whereas ferritin, calcium, and vitamin D deficiencies persisted or worsened, particularly in the nonintervention group.

CONCLUSIONS

An appropriately implemented mHealth intervention can improve antenatal vitamin D supplementation. Affordable, accessible, and personalized counseling through mHealth could ameliorate micronutrient status during pregnancy.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04216446; https://clinicaltrials.gov/study/NCT04216446.

Machine Learning-Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts: Model Development and Validation Study

BACKGROUND

Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus (T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple risk factors.

OBJECTIVE

This study aimed to assess the validity and use of an ML model for predicting the 3-year incidence of ND in patients with T2DM.

METHODS

We used data from 2 independent cohorts-the discovery cohort (1 hospital; n=22,311) and the validation cohort (2 hospitals; n=2915)-to predict ND. The outcome of interest was the presence or absence of ND at 3 years. We selected different ML-based models with hyperparameter tuning in the discovery cohort and conducted an area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort.

RESULTS

The study dataset included 22,311 (discovery) and 2915 (validation) patients with T2DM recruited between 2008 and 2022. ND was observed in 133 (0.6%) and 15 patients (0.5%) in the discovery and validation cohorts, respectively. The AdaBoost model had a mean AUROC of 0.82 (95% CI 0.79-0.85) in the discovery dataset. When this result was applied to the validation dataset, the AdaBoost model exhibited the best performance among the models, with an AUROC of 0.83 (accuracy of 78.6%, sensitivity of 78.6%, specificity of 78.6%, and balanced accuracy of 78.6%). The most influential factors in the AdaBoost model were age and cardiovascular disease.

CONCLUSIONS

This study shows the use and feasibility of ML for assessing the incidence of ND in patients with T2DM and suggests its potential for use in screening patients. Further international studies are required to validate these findings.

Diagnostic value of isolated plasma biomarkers and its combination in neurodegenerative dementias: A multicenter cohort study

BACKGROUND

Plasma amyloid-β (Aβ), phosphorylated tau-181 (p-tau181), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) potentially aid in the diagnosis of neurodegenerative dementias. We aim to conduct a comprehensive comparison between different biomarkers and their combination, which is lacking, in a multicenter Chinese dementia cohort consisting of Alzheimer's disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP).

METHODS

We enrolled 92 demented patients [64 AD, 16 FTD, and 12 PSP with dementia] and 20 healthy controls (HC). Their plasma Αβ, p-tau181, NfL, and GFAP were detected by highly sensitive-single molecule immunoassays. Αβ pathology in patients was measured by cerebrospinal fluid or/and amyloid positron emission tomography.

RESULTS

All plasma biomarkers tested were significantly altered in dementia patients compared with HC, especially Aβ42/Aβ40 and NfL showed significant performance in distinguishing AD from HC. A combination of plasma Aβ42/Aβ40, p-tau181, NfL, and GFAP could discriminate FTD or PSP well from HC and was able to distinguish AD and non-AD (FTD/PSP).

CONCLUSIONS

Our results confirmed the diagnostic performance of individual plasma biomarkers Aβ42/Aβ40, p-tau181, NfL, and GFAP in Chinese dementia patients and noted that a combination of these biomarkers may be more accurate in identifying FTD/PSP patients and distinguishing AD from non-AD dementia.

Bodybuilders and high-level meat consumers' behavior towards rabbit, beef, chicken, turkey, and lamb meat: A comparative review

In bodybuilders' diets, protein plays a crucial role in supporting muscle growth and repairing damaged muscle tissue. These individuals meet their protein needs by combining dietary sources with supplements. Animal-based proteins are often preferred over plant-based proteins because they are believed to better support muscle protein synthesis. This review explores the meat consumption patterns of bodybuilders and high-level meat consumers, focusing on rabbit, beef, chicken, turkey, and lamb. We describe and compare the types of meat bodybuilders commonly consume and provide an overview of protein supplements, including meat-based options, plant-based alternatives, and whey-based products. Our aim is to gain insight into the dietary preferences of bodybuilders and high-level meat consumers, considering their nutritional requirements and the potential effect on the meat industry. We conducted an extensive search across various databases, including Scopus, Web of Science, PubMed, and Google Scholar. We found that individual choices vary based on factors such as attitudes, trust, taste, texture, nutritional content, ethical considerations, and cultural influences. Nutritional factors, including protein content, amino acid profiles, and fat levels, significantly influence the preferences of bodybuilders and high-level meat consumers. However, it is crucial to maintain a balance by incorporating other essential nutrients such as carbohydrates, healthy fats, vitamins, and minerals to ensure a complete and balanced diet. The findings from this review can inform strategies and product development initiatives tailored to the needs of bodybuilders and discerning meat enthusiasts.

The association between plasma angiopoietin-like protein 4, glucose and lipid metabolism during pregnancy, placental function, and risk of delivering large-for-gestational-age neonates

BACKGROUND

Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like protein 4 (ANGPTL4), a protein involved in lipid and glucose metabolism during pregnancy, placental function, growth factors, and the risk of LGA.

METHODS

We conducted a prospective cohort study and recruited women with singleton pregnancies at the National Taiwan University Hospital between 2013 and 2018. First trimester maternal plasma ANGPTL4 concentrations were measured.

RESULTS

Among 353 pregnant women recruited, the LGA group had higher first trimester plasma ANGPTL4 concentrations than the appropriate-for-gestational-age group. Plasma ANGPTL4 was associated with hemoglobin A1c, post-load plasma glucose, plasma triglyceride, plasma free fatty acid concentrations, plasma growth hormone variant (GH-V), and birth weight, but was not associated with cord blood growth factors. After adjusting for age, body mass index, hemoglobin A1c, and plasma triglyceride concentrations, plasma ANGPTL4 concentrations were significantly associated with LGA risk, and its predictive performance, as measured by the area under the receiver operating characteristic curve, outperformed traditional risk factors for LGA.

CONCLUSIONS

Plasma ANGPTL4 is associated with glucose and lipid metabolism during pregnancy, plasma GH-V, and birth weight, and is an early biomarker for predicting the risk of LGA.

Recurring food source-based Listeria outbreaks in the United States: An unsolved puzzle of concern?

Recurring Listeria outbreaks in the United States is a growing public healthcare concern. Although no associated reported death, 17 were hospitalized out of the 18 reported illnesses in the recent outbreak in 15 US states. The United States has experienced about 30 Listeria outbreaks in the last decade with 524 Listeriosis cases and 80 deaths. The identified origin were ice cream, leafy greens, mushroom, meat slice, dairy products like cheese, packaged salads, cooked chicken, hard-boiled egg, pork product, frozen vegetable, raw milk, packaged caramel apple, bean sprout and soya products. Although rare, Listeria may lead to serious illness (invasive listeriosis) or death. Listeriosis is critically harmful and medically complicated, especially in the pregnant, the old above 65 years and in the immunocompromised. It could cause premature birth, miscarriage or even neonatal death. Hospitalization is often necessary in the geriatric, being fatal at times. Among Listeria sp., Listeria monocytogenes is often human infection-associated. It is a gram-positive, non-sporulating, motile bacillus opportunistic pathogen. Food-borne listeriosis is often associated with frozen foods due to its ability to thrive at low temperatures. Hypervirulent strains of L. monocytogenes with an ability to infect the respiratory system (the lungs) was recently reported in the coronavirus disease-19 patients during the pandemic. L. monocytogenes seemed to have developed antimicrobial resistance to ciprofloxacin and meropenem, possibly acquired through the food chain. An early onset of listeriosis in the newborn is evident in the first 7 days postparturition. As the bacteria colonize the genitourinary tract, majority of such cases result from teratogenic transfer during vaginal delivery. Premature newborns, neonates born outside healthcare facilities and low-birth-weight babies were increasingly predisposed to an early onset of listeriosis. Listeria outbreaks were earlier reported in South Africa, Australia and Europe, with an unclear origin of the outbreaks. Social media updates about such outbreaks, the most likely food source, and measures to self-protect are suggested as preventive measures. The article deals on various such aspects related to listeriosis primarily originating from food, to ensure better public healthcare and human wellness.

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Association of hypertension in pregnancy with serum electrolyte disorders in late pregnancy among Cameroonian women

Multiple electrolyte disorders, including sodium, potassium and calcium disorders, have been associated with hypertension in pregnancy. Most of these studies failed to evaluate the combined effect of low and high sodium, potassium, calcium and chloride ion concentrations on hypertension in pregnancy. This study evaluates the combined effect of these ion categories (low, normal, high) on hypertension in pregnancy. Biochemical ion assays and blood pressure measurements were carried out on 1074 apparently healthy pregnant women in late third trimester. Serum potassium, sodium, chloride, and ionised calcium were measured by ion-selective electrode potentiometry, while total plasma calcium was measured by absorption spectrophotometry. Hypertension in pregnancy was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg. The prevalence of hyponatraemia, hypokalaemia, hypochloraemia, ionised hypocalcaemia and total hypocalcaemia in late pregnancy was 1.30 [0.78-2.18]%, 3.55 [2.60-4.84]%, 1.96 [1.28-2.97]%, 1.49 [0.92-2.21]% and 43.58 [40.64-46.56]%, respectively. Hypernatraemia, hyperkalaemia, hyperchloraemia, ionised hypercalcaemia and total hypercalcaemia were found in 1.49 [0.92-2.41]%, 2.34 [1.59-3.43]%, 4.38 [3.31-5.77]%, 39.94 [37.06-42.90]%, 2.79 [1.96-3.96]% of the participants, respectively. The prevalence of hypertension in pregnancy was 7.17 [5.77-8.87]%. When ion categories were considered in multiple logistic regression, only ionised and total calcium had significant associations with hypertension in pregnancy. Women with ionised hypercalcaemia had lower odds of hypertension in pregnancy (AOR = 0.50 [0.29-0.87], p-value = 0.015), and women with total hypocalcaemia had higher odds of hypertension in pregnancy (AOR = 1.99 [1.21-3.29], p-value = 0.007), compared to women with ionised and total normocalcaemia, respectively. Increasing kalaemia was associated significantly with higher odds of hypertension in pregnancy; however, kalaemia below and above the normal concentrations had no significant association with hypertension. Nonetheless, participants with kalaemia ≤ 3.98 mmol/L, had lower odds of hypertension in pregnancy compared with those with higher kalaemia (OR = 0.40 [0.24-0.66], p-value = 0.0003). Calcium disorders remain the most frequent electrolyte disorders in pregnancy. When normal cut-offs are considered for calcium and other ions, only ionised and total calcium influence the occurrence of hypertension in pregnancy. Kalaemia seems to affect hypertension in pregnancy but primarily within its normal concentrations. Serum electrolyte follow-up is indispensable for a proper pregnancy follow-up.