Impacts of Anthropometric, Biochemical, Socio-demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

Ruminal microbiota and muscle metabolome characteristics of Tibetan plateau yaks fed different dietary protein levels

Introduction

The dietary protein level plays a crucial role in maintaining the equilibrium of rumen microbiota in yaks. To explore the association between dietary protein levels, rumen microbiota, and muscle metabolites, we examined the rumen microbiome and muscle metabolome characteristics in yaks subjected to varying dietary protein levels.

Methods

In this study, 36 yaks were randomly assigned to three groups (n = 12 per group): low dietary protein group (LP, 12% protein concentration), medium dietary protein group (MP, 14% protein concentration), and high dietary protein group (HP, 16% protein concentration).

Results

16S rDNA sequencing revealed that the HP group exhibited the highest Chao1 and Observed_species indices, while the LP group demonstrated the lowest. Shannon and Simpson indices were significantly elevated in the MP group relative to the LP group (P < 0.05). At the genus level, the relative abundance of Christensenellaceae_R-7_group in the HP group was notably greater than that in the LP and MP groups (P < 0.05). Conversely, the relative abundance of Rikenellaceae_RC9_gut_group displayed an increasing tendency with escalating feed protein levels. Muscle metabolism analysis revealed that the content of the metabolite Uric acid was significantly higher in the LP group compared to the MP group (P < 0.05). The content of the metabolite L-(+)-Arabinose was significantly increased in the MP group compared to the HP group (P < 0.05), while the content of D-(-)-Glutamine and L-arginine was significantly reduced in the LP group (P < 0.05). The levels of metabolites 13-HPODE, Decanoylcarnitine, Lauric acid, L-(+)-Arabinose, and Uric acid were significantly elevated in the LP group relative to the HP group (P < 0.05). Furthermore, our observations disclosed correlations between rumen microbes and muscle metabolites. The relative abundance of NK4A214_group was negatively correlated with Orlistat concentration; the relative abundance of Christensenellaceae_R-7_group was positively correlated with D-(-)-Glutamine and L-arginine concentrations.

Discussion

Our findings offer a foundation for comprehending the rumen microbiome of yaks subjected to different dietary protein levels and the intimately associated metabolic pathways of the yak muscle metabolome. Elucidating the rumen microbiome and muscle metabolome of yaks may facilitate the determination of dietary protein levels.

Beyond Boundaries: A Comprehensive Review of Anthropometric Indices in Urban and Rural India

This comprehensive review examines anthropometric indices in the context of urban and rural India, shedding light on the dynamic interplay between lifestyle, socio-economic factors, and environmental influences on health outcomes. Analyzing indicators such as Body Mass Index (BMI), waist-to-hip ratio (WHR), and mid-upper arm circumference (MUAC), the study reveals distinct disparities between urban and rural populations. While rural areas face the challenges of undernutrition and stunting, urban environments grapple with the escalating prevalence of obesity and non-communicable diseases. The implications for public health underscore the need for tailored interventions, encompassing nutritional education, equitable healthcare access, and lifestyle interventions. The call-to-action advocates for collaborative efforts among policymakers, healthcare professionals, researchers, and communities to implement evidence-based strategies, advocate for policy reforms, and continually monitor anthropometric trends. This review serves as a roadmap for fostering healthier communities in India by addressing anthropometric disparities and steering toward a more equitable and sustainable future.

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

BACKGROUND

Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information.

OBJECTIVE

To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.

METHODS

Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.

RESULTS

Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.

CONCLUSIONS

This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

Time-restricted feeding affects transcriptomic profiling of hypothalamus in pigs through regulating aromatic amino acids metabolism

BACKGROUND

Time-restricted feeding (TRF) is an effective means that can efficiently regulate the metabolism and health of animals and humans. However, the effect of TRF on hypothalamic function remains unclear.

RESULTS

Results showed that TRF significantly increased the activities of digestive enzymes lipase, maltase in the duodenum and lipase, trypsin in the pancreas whereas significantly decreased serum gastrointestinal hormones gastrin, glucagon-like peptide-1, cholecystokinin, peptide YY, and ghrelin. Metabolites related to amino acid metabolism, including citrulline, kynurenine, N-acetylleucine, l-tryptophan, and l-tyrosine, significantly increased in the TRF group. Differential metabolites were mainly enriched in phenylalanine, tyrosine, and tryptophan biosynthesis and tryptophan metabolism. Transcriptomic analysis of hypothalamus showed that a total of 462 differentially expressed genes (DEGs) were significantly changed by TRF. In particular, DEGs such as DDC, TH, GOT2, and DBH involved in aromatic amino acid metabolism pathways were significantly downregulated, whereas the expression of CYP1B1 was significantly upregulated. Moreover, DEGs (PDYN and PPP3CA) involved in amphetamine addiction and cocaine addiction were also downregulated in the TRF group.

CONCLUSION

Taken together, these results suggested that TRF improved the digestion and absorption of nutrients and thus increased the accessibilities of aromatic amino acids. The increasing of circulating aromatic amino acids might mediate the regulatory neuroendocrine effects of TRF regimes on the hypothalamus functions, especially on drug addictions. This study reveals a possible mechanism underlying the effects of regulating feeding patterns on the function of the hypothalamus by altering aromatic amino acids metabolism. © 2022 Society of Chemical Industry.

Prevalence of Underweight, Overweight and Obesity among Adults in Urban Bissau, Western Africa

Overweight and obesity affect a large proportion of the population and are important causes of death in both developed and low- and middle-income countries. In Guinea-Bissau, there are no previous population-based studies assessing this phenomenon. Therefore, we aimed to quantify the prevalence of underweight, overweight, and obesity among adults in Bissau. A stratified and cluster sample of 935 adults was assembled in 2021 and was evaluated using standardized questionnaires and anthropometric measurements, following the World Health Organization Stepwise Approach to Chronic Disease Risk Factor Surveillance. Underweight, obesity, and overweight were defined by body mass index based on the World Health Organization definitions. The prevalence of overweight and obesity was 48.7% among women and 25.0% among men. The proportion of women with abdominal obesity was 14 times higher than it was in men (35.3% vs. 2.5%). The prevalence of overweight and obesity increased with age and income. Underweight was more prevalent in the age group of 18 to 24 years (18.4% in women and 28.9% in men) and was less frequent among individuals with higher socioeconomic status. In conclusion, the prevalence of overweight and obesity is similar to the trends that are observed in many other urbanized populations in Africa and is already a major public health issue in urban Guinea-Bissau.

Redesigning Portable Health Clinic Platform as a Remote Healthcare System to Tackle COVID-19 Pandemic Situation in Unreached Communities

Medical staff carry an inordinate risk of infection from patients, and many doctors, nurses, and other healthcare workers are affected by COVID-19 worldwide. The unreached communities with noncommunicable diseases (NCDs) such as chronic cardiovascular, respiratory, endocrine, digestive, or renal diseases became more vulnerable during this pandemic situation. In both cases, Remote Healthcare Systems (RHS) may help minimize the risk of SARS-CoV-2 transmission. This study used the WHO guidelines and Design Science Research (DSR) framework to redesign the Portable Health Clinic (PHC), an RHS, for the containment of the spread of COVID-19 as well as proposed corona logic (C-Logic) for the main symptoms of COVID-19. Using the distributed service platform of PHC, a trained healthcare worker with appropriate testing kits can screen high-risk individuals and can help optimize triage to medical services. PHC with its new triage algorithm (C-Logic) classifies the patients according to whether the patient needs to move to a clinic for a PCR test. Through modified PHC service, we can help people to boost their knowledge, attitude (feelings/beliefs), and self-efficacy to execute preventing measures. Our initial examination of the suitability of the PHC and its associated technologies as a key contributor to public health responses is designed to "flatten the curve", particularly among unreached high-risk NCD populations in developing countries. Theoretically, this study contributes to design science research by introducing a modified healthcare providing model.