Interaction between micronutrients and lipid profile in prediabetes and diabetes among school-aged children (5-9 y) in India.

OBJECTIVES: Lipids and micronutrients play a major role in the pathophysiology of diabetes, and several studies have established the association between lipids and diabetes. The aim of this study was to determine the interaction between lipid profile and micronutrient status with different prediabetes and diabetes diagnosis criteria among school-aged children in India. METHODS: The data used in this study was from Comprehensive National Nutritional Survey conducted in India from 2016 to 2018. Glycosylated hemoglobin values and fasting blood glucose were used to classify normal, prediabetes, and diabetes. The interaction analysis between the lipid profile and eight micronutrients was conducted using multiple logistic regression analyses, and the predicted probabilities were determined. RESULTS: Among micronutrients, the highest deficiency was observed for hemoglobin (27%), and in the lipid profile, triacylglycerol was high in 34% of children. The interaction between high total cholesterol and vitamin B12 deficiency showed the highest average probability for prediabetes (66%). The highest average probability for diabetes was observed from the interaction between normal high-density lipoprotein and vitamin A deficiency (3%). CONCLUSION: The interaction between micronutrients and lipids suggests complex multidimensional pathways involving folate, vitamin B12, ferritin, zinc, hemoglobin, and iodine deficiencies. These interactions should be considered when planning diabetes management strategies.

Prevalence of pre-diabetes and diabetes among school-age children and adolescents of India: A brief report.

The prevalence study of pre-diabetes and diabetes among children and adolescents is crucial for early detection and intervention, public health resource allocation and monitoring the trend. The national prevalence of pre-diabetes and diabetes among school-age children was 15.35% and 0.94%, whereas, among adolescents, the prevalence was 16.18% and 0.56%, respectively.

Impact of Community Based Nutrition Intervention in the Prevention and Control of Anaemia Among Reproductive Aged Women of India.

INTRODUCTION: Maternal and child under-nutrition is particularly widespread in low and middle-income nations, increasing the overall disease burden due to poor nutritional status. The aim of this study was to develop nutrition intervention for the prevention and control of anaemia among women of reproductive age. METHODS: Community-based intervention study was conducted among 443 women of reproductive age group (15-49 years) to determine the effectiveness of a 6-month nutrition intervention package. The nutrition intervention was developed by using Precede-Proceed model and the trans-theoretical model of behavior change. Multi-channel communication approach was adopted and nutrition intervention package was provided. Assessment of haemoglobin, red blood cells, platelet, ferritin, folate, vitamin B12, haematocrit test, mean corpuscular volume, mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration, red cell distribution width and total leucocyte count was compared at the baseline and endline after the intervention among the participants. The chi-square test of independence and t-test were performed. RESULTS: The only mean ferritin level shows significant improvement (p < 0.001). A significant decrease (~ 15%, p = 0.027) in anaemia was observed after the intervention. CONCLUSIONS: Improvement in anaemic status of women was observed. National schemes and programs require a more robust strategical implementation like food fortification/bio fortification and behaviour change communication at village level to enhance the availability and accessibility of fortified food.

Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents.

BACKGROUND AND OBJECTIVES: Pre-diabetes has been identified as an intermediate diagnosis and a sign of a relatively high chance of developing diabetes in the future. Diabetes has become one of the most frequent chronic disorders in children and adolescents around the world; therefore, predicting the onset of pre-diabetes allows a person at risk to make efforts to avoid or restrict disease progression. This research aims to create and implement a cross-validated machine learning model that can predict pre-diabetes using non-invasive methods. METHODS: We have analysed the national representative dataset of children and adolescents (5-19 years) to develop a machine learning model for non-invasive pre-diabetes screening. Based on HbA1c levels the data (n = 26,567) was segregated into normal (n = 23,777) and pre-diabetes (n = 2790). We have considered eight features, six hyper-tuned machine learning models and different metrics for model evaluation. The final model was selected based on the area under the receiver operator curve (AUC), Cohen's kappa and cross-validation score. The selected model was integrated into the screening tool for automated pre-diabetes prediction. RESULTS: The XG boost classifier was the best model, including all eight features. The 10-fold cross-validation score was highest for the XG boost model (90.13%) and least for the support vector machine (61.17%). The AUC was highest for RF (0.970), followed by GB (0.968), XGB (0.959), ETC (0.918), DT (0.908), and SVM (0.574) models. The XGB model was used to develop the screening tool. CONCLUSION: We have developed and deployed a machine learning model for automated real-time pre-diabetes screening. The screening tool can be used over computers and can be transformed into software for easy usage. The detection of pre-diabetes in the pediatric age may help avoid its enhancement. Machine learning can also show great competence in determining important features in pre-diabetes.

Comprehensive overview of smartphone applications delivering child nutrition information.

Numerous smartphone-based applications that guide parenting, child nutrition, and child health-related knowledge are available. Here, we reviewed the applications available in the Google Play Store for child nutrition, primarily focused on children aged <5 y. The keywords used in the search were "child nutrition," "child nutrition status assessment," and "parenting." We identified 370 apps from the play store and 33 qualified for the review. Among 33 apps, 3 were not updated in the last 3 mo, and 19 did not mention their source of information. Four apps did not require the child's name, date of birth, and sex for logging in. Twenty-three apps were available in English only. The output features of the selected apps were food, growth, development and vaccine trackers, data export, reminders, meal planner, feeding tips, list of food, recipes details, information about nutrients, and question/answer session with the expert. Only eight apps provided access to consultation with experts and three suggested nutrient requirements of the child. Three apps scored similarly based on features, although the feature types differed. Findings from this review suggest that the apps do not follow any uniform guidelines for delivering the child nutrition information to the caregivers. About 50% of apps did not mention the consulted source for its development, indicating the unavailability of uniform guidelines or policy documents for child nutrition app development. App-based intervention studies are recommended to assess the effectiveness of child nutrition/health smartphone applications.

Determining the nutritional immunity information-seeking behaviour during the COVID-19 pandemic in India: a Google Trends data analysis.

OBJECTIVE: During COVID-19, the Internet was a prime source for getting relevant updates on guidelines and desirable information. The objective of the present study was to determine the nutritional immunity information-seeking behaviour during COVID-19 in India. DESIGN: Google Trends (GTs) data on relevant COVID-19 and nutritional topics were systematically selected and retrieved. Data on newly reported COVID-19 cases were also examined on a daily basis. The cross-correlation method was used to determine the correlation coefficient between the selected terms and daily new COVID-19 cases, and the joinpoint regression models were utilised to measure monthly percent change (MPC) in relative search volumes (RSV). SETTING: Online. PARTICIPANTS: People using Google search during the period 1 January 2020-31 August 2020 in India. RESULTS: The date of peak searches can be attributed to the COVID-19 guidelines announcement dates. All the nutritional terms showed a significant increase in average monthly percentage change. The higher than the average daily rise in COVID-19 cases leads to a higher than average increase in RSV of nutritional terms with the greatest association after 14-27 d. The highest mean relative search volume for nutritional terms was from Southern India (49·34 ± 7·43), and the lowest was from Western India (31·10 ± 6·30). CONCLUSION: There was a significant rise in the Google searches of nutritional immunity topics during COVID-19 in India. The local/regional terms can be considered for better outreach of public health guidelines or recommendations. Further automation of Google Trends using programming languages can help in real-time monitoring and planning various health/nutritional events.

Biological attributes of age and gender variations in Indian COVID-19 cases: A retrospective data analysis.

BACKGROUND: The associated risk factors, co-morbid conditions and biological differences varying with gender and age might be the cause of higher COVID-19 infection and deaths among males and older persons. The objective of this study was to predict and specify the biological attributes of variation in age and gender-based on COVID-19 status (deceased/recovered). METHODS: In this retrospective study, the data was extracted from a recognised web-based portal. A total of 112,860 patients' record was filtered out and an additional 9131 records were separately analysed to examine age and gender relationship with patient's COVID-19 status (recovered/deceased). Chi-square, t-test, binary logistic regression, and longitudinal regression analysis were conducted. RESULTS: The male COVID-19 cases (65.39%) were more than females (34.61%) and mean age of infected and recovered patients was 39.47 ± 17.59 years and 36.85 ± 18.51 years respectively. The odds for infection was significantly higher among females for lower age categories, which declines with age. The age-adjusted odds for recovery were significantly higher among females (O.R. = 1.779) and odds for recovery was highest in 5-17 years age category (O.R. = 88.286) independent of gender. CONCLUSION: The chances of being COVID-19 infected was higher for females of lower age categories (<35 years) which decreases with age. The odds for recovery among females was significantly higher than males. The chances of recovery declines with increasing age and the variation could be attributed to the biological differences between age categories and gender.

Estimates of malnutrition and risk of malnutrition among the elderly (≥60 years) in India: A systematic review and meta-analysis.

BACKGROUND: Death is inevitable, but healthy ageing is possible with proper nutrition and health care. This systematic review and meta-analysis conducted to estimate the nation-wide prevalence and malnutrition and risk of malnutrition among the elderly in India. METHODS: PubMed, EMBASE, Web of Science, Cochrane`s library, Google Scholar were searched for the articles reporting the prevalence of malnutrition among the elderly using MNA or MNA-SF tools. The study published between the year 2010-2019 were included. Sensitivity analysis, quality assessment was done using standard methods. The publication biasness was also determined using Doi plot and LFK index. The pooled prevalence was reported with effect size and considering the random effect model and quality effect model. The subgroup analysis was also conducted for gender, study setting, study area and study regions. RESULTS: The prevalence of malnutrition and risk of malnutrition among the elderly was 18.29% and 48.17% respectively. The prevalence of malnutrition was higher among female (16.67%), clinic setting (28.87%), urban areas (19.29%) and northern region (27.37%) of India. This trend differs with the prevalence of risk of malnutrition. Meta-regression analysis shows a region-based prediction of malnutrition and setting based prediction of risk of malnutrition. CONCLUSION: The prevalence of malnutrition was not considerably higher among the elderly population but about half of the elderly population were at risk of malnutrition. This trend differs with the gender, study setting, study area and study region. Additional study using other nutritional assessment tools were needed. Intervention studies among the elderly were recommended.