Lipid Accumulation Product: Reliable Marker for Cardiovascular Risk Detection?

The Lipid Accumulation Product (LAP) is a clinical marker of visceral obesity and has been proposed as a simple, inexpensive, and accurate tool to estimate cardiovascular risk and mortality. The aim of this study was to verify the association of LAP with anthropometric, biochemical, visceral adiposity index and IR in adults and the elderly. This single cross-section center clinical study, with experimental, analytical, primary, and observational design, included 210 participants. Anthropometric (Body Mass Index (BMI), Waist Circumference (WC), and Neck Circumference (NC)), LAP, Visceral Adipose Index (VAI), and biochemical parameters (fasting glycemia, insulinemia (to calculate the Homa-IR index), total cholesterol, LDL-c, HDL-c, and triglycerides) were evaluated. The results showed that by separating the sample into three groups (adequate BMI and WC, adequate BMI and elevated WC, and elevated BMI and WC), the group with high BMI and WC showed a high value of LAP and VAI compared to the other groups, with a significant difference. Still, the data show a positive and significant correlation when relating the LAP with VAI, HOMA-IR, BMI, WC, NC, total cholesterol, triglycerides, and Diastolic Blood Pressure. It also showed an inversely proportional relationship when associating LAP with HDL-c (p < 0.0001). Thus, we show that LAP is closely related to visceral adiposity, IR, altered lipid parameters, and blood pressure, especially diastolic in the patients included in our study. For these reasons, we suggest that LAP is a reliable indicator of promising visceral adiposity for early detection of cardiovascular risk in the adult and senior population.

Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Association between Chinese visceral adiposity index and risk of cardiometabolic multimorbidity in middle-aged and elderly Chinese population:Evidence from a national longitudinal study

Background There are several surrogate indicators of abdominal fat deposition,including tri-ponderal mass index(TMI),lipid accumulation product(LAP),and the Chinese visceral adiposity index(CVAI).In spite of this,it remains unclear whether these indices have a longitudinal relationship with the prevalence of cardiometabolic multimorbidity(CM),a pressing global health issue.This research investigated the association between CVAI and CM compared to other indicators of visceral obesity.Methods 6638 participants(aged>45)from the China Health and Retirement Longitudinal Study(CHARLS)were analyzed for incident CM.Cox proportional models were adopted to explore whether the level of CVAI was correlated with the risk of CM.Harrell's concordance statistic(C-statistic)was applied to compare predictive values.Sensitivity and subgroup analyses were implemented for the steadiness of the results.Results Over 4 years,266(4.01%)participants developed CM.A 1-standard deviation(SD)increase in the levels of CVAI,body mass index(BMI),LAP,and TMI was associated with greater CM risk after adjusting for confounders[hazard ratios(HRs):2.20,95%confidence interval(CI):1.88-2.57,1.92(95%CI:1.55-2.38),1.20(95%CI:1.12-1.27),and 1.50(95%CI:1.35-1.66),respectively].CVAI outperformed other indices in predictive performance.Subgroup analysis revealed younger participants or those living alone were more prone to developing CM.Results were potent after finishing all sensitivity analyses.Conclusions The study highlighted a positive correlation between the level of CVAI and CM risk.CVAI's superior predictive performance positions it as a reliable indicator for identifying individuals at heightened CM risk.