Association of metabolic syndrome traits with urinary biomarkers in Japanese adults

Background Although metabolic syndrome traits are risk factors for chronic kidney disease, few studies have examined their association with urinary biomarkers. Methods Urinary biomarkers, including A-megalin, C-megalin, podocalyxin, albumin, α1-microglobulin, β2-microglobulin, and N-acetyl-β-D-glucosaminidase, were cross-sectionally assessed in 347 individuals (52.7% men) with a urine albumin-to-creatinine ratio (ACR) < 300 mg/g in a health checkup. Metabolic syndrome traits were adopted from the National Cholesterol Education Program (third revision) of the Adult Treatment Panel criteria modified for Asians. Results Participants had a mean body mass index, estimated glomerular filtration rate (eGFR), and median ACR of 23.0 kg/m2, 74.8 mL/min/1.73 m2, and 7.5 mg/g, respectively. In age- and sex-adjusted logistic regression analysis, A-megalin and albumin were significantly associated with the clustering number of metabolic syndrome traits (3 or more). After further adjustment with eGFR, higher quartiles of A-megalin and albumin were each independently associated with the clustering number of metabolic syndrome traits (adjusted odds ratio for A-megalin: 1.30 per quartile, 95% CI 1.03–1.64; albumin: 1.42 per quartile, 95% CI 1.12–1.79). Conclusions Both urinary A-megalin and albumin are associated with the clustering number of metabolic syndrome traits. Further research on urinary A-megalin is warranted to examine its role as a potential marker of kidney damage from metabolic risk factors.

Severe Hypoglycemia Increases Dementia Risk and Related Mortality: A Nationwide, Population-Based Cohort Study

Context There are few studies focused on the relationship between hypoglycemia and new-onset dementia in patients with type 2 diabetes and no study regarding mortality of dementia after hypoglycemia. Objective We investigated the effect of severe hypoglycemia on dementia subtypes and its relation to overall mortality in patients with type 2 diabetes. Methods We evaluated incident dementia, including Alzheimer disease and vascular dementia, among health checkup participants aged 40 years or older in the National Health Insurance System in Korea from January 2009 to December 2015. Episodes of severe hypoglycemia were examined for 3 years before the date of the health checkup. Results Among 2 032 689 participants (1 172 271 men, 860 418 women), 14 443 (0.7%) experienced severe hypoglycemia, during a mean follow-up period of 6.9 ± 1.7 years. Individuals in the severe hypoglycemia group were more likely to be diagnosed with dementia compared to individuals without severe hypoglycemia (23.3% vs 7.3%; P &lt; .001) and the overall incidence of Alzheimer disease was higher than vascular dementia. Dementia risk rose with increasing number of severe hypoglycemic episodes (1 episode [hazard ratio (HR) = 1.54; 95% CI, 1.48-1.60], 2 or more episodes [HR = 1.80; 95% CI, 1.66-1.94]). Overall mortality was higher in participants with dementia, but without severe hypoglycemia (HR = 2.03; 95% CI, 1.96-2.10) and severe hypoglycemia, but without dementia (HR = 4.24; 95% CI, 4.29-4.40), and risk of death was highest in those with both severe hypoglycemia and dementia (HR = 5.08; 95% CI, 4.83-5.35). Conclusion Severe hypoglycemia is associated with dementia, especially Alzheimer disease and mortality; together, they have an additive effect on overall mortality.

Assessment of factors affecting flicker ERGs recorded with RETeval from data obtained from health checkup screening

The purpose of this study was to determine the ocular, sex- and age-specific, anthropometric, and hematologic factors that affect the implicit times and amplitudes of the flicker ERGs recorded with the RETeval system from individuals 40- to 89-years-of-age. Flicker ERGs were recorded with the RETeval system from 330 individuals who had normal fundus and OCT images. Univariate and multivariate regression analyses were performed to identify factors associated with the implicit times and amplitudes of the RETeval flicker ERGs. Univariate regression analyses showed significant correlations between the implicit times and the BCVA, age, axial length, blood sugar level, and BUN in both eyes. Multivariate regression analyses identified age and axial length as two independent factors that were significantly correlated with the implicit times of the RETeval flicker ERGs. Univariate regression analyses also showed significant correlations between the amplitudes and age, platelet count, HDL level, and creatinine level in both eyes. However, smoking habits, body mass index, and blood pressure were not correlated with the RETeval flicker ERGs. We conclude that age and some ophthalmologic and hematologic findings except for anthropometric findings were suggested to significantly affect the measurements of the RETeval flicker ERGs.

Health Monitoring System Using IoT

Health on of the key aspects of any human’s life and one needs to keep up good health to make progress in life. We see most of the people facing health issues during different times during their life and it is their first job as an individual to keep up their health. As weare already seeing the novel coronavirus has devastated the world for the past few months and continues to do so. We see many cases like people living in distant villages and towns where there are no proper medical facilities to have a health check-up once in a while, they need to travel long distances to get a basic health checkup done. Especially the old people who cannot travel much due to their age. A human's health can be carefully monitored and a reasonable outcome can be predicted to avoid unfortunate deaths or misfortunate occurrences by measuring temperature, sp02, or pulse and Strain upon the muscles. These factors are being collected by various devices, but have never been classified and carefully monitored for anomalies. So this system is the best option to counter the situation as it collects data and stores it in the webserver and also gives alerts. Keywords: Health, Sensors, Internet of things

Immunogenicity after Second ChAdOx1 nCoV-19 (AZD1222) Vaccination According to the Individual Reactogenicity, Health Status and Lifestyle

The immune-acquired responses after vaccination vary depending on the type of vaccine and the individual. The purpose of this study was to investigate the relationship between the acquisition of immunity and the side effects, health status, and lifestyle after completion of the second dose of AZD1222. Blood samples were collected after a second dose of AZD1222. The Euroimmun Anti-SARS-CoV-2 ELISA (IgG) for anti-S1 antibody, the cPASS SARS-CoV-2 neutralizing antibody detection kit for the surrogate virus neutralization test, and the T-spot Discovery SARS-CoV-2 kit were used to identify cellular immunogenicity. Patient experience of adverse effects was investigated using questionnaires. Information on health status and lifestyle were collected from the most recent health checkup data. Generally, females experience more reactogenicity in both intensity and duration. The rash of the first shot and chills of the second shot were associated with humoral immunity. However, comprehensive adverse effects had no correlation with humoral and cellular immunity. The T-spot-positive group had a higher creatinine level, which reflects muscle mass, than the T-spot-negative group. Males presented a higher level of T-spot assays. Body mass index and age were negatively correlated with the T-spot assay and anti-S1 antibody, respectively. Immune acquisition after the second AZD1222 shot was not associated with reactogenicity. However, individuals’ sex, age, and BMI were found to be associated with immunogenicity after vaccination.

Factors of Having Difficulties Raising 3-Year-Old Children in Japan: Usefulness of Maternal and Child Health Information Accumulated by the Local Government

Background: Difficulties raising children may be associated with depressive tendencies and abuse by parents, for which maternal and child health information may be useful. We clarified factors related to difficulties in raising children at the time of the 3-year-old child health checkup. Method: This was a retrospective cohort study. We used maternal and child health information collected from the time of pregnancy notification until the 3-year-old child health checkup. The subjects were the parents of 507 children who were born and eligible for the 3-year-old child health checkup between September 2013 and October 2017. Logistic regression and ROC analyses were performed. The dependent variable was “having difficulties raising children at the 3-year-old health checkup”. Result: Eleven factors were clarified as risk factors. Three major factors among them were having difficulties raising children at the 18-month-old checkup (adOR, 6.3; 95%CI, 3.0–13.9), actions are at the child’s own pace and adult instructions are difficult to follow at the 18-month-old health checkup (adOR, 5.0; 95%CI, 1.3–25.4), and EPDS score ≥ 2 (adOR, 3.4; 95%CI, 1.5–8.1). The AUC of this predictive model was 0.86. At a cutoff value of 0.387, the sensitivity was 79.7% and the specificity was 77.6%. Conclusion: Having difficulties raising children at the 3-year-old health checkup has factors from the time of pregnancy and requires continued support. It was possible and useful to use maternal and child health information when screening high-risk parents.