The Predictive Value of Visceral Adiposity Index and Lipid Accumulation Index for Microalbuminuria in Newly Diagnosed Type 2 Diabetes Patients

Does excess body weight accelerate immune aging?

BACKGROUND

Overweight and obesity in older adults increase the risk of a range of comorbidities by sustaining chronic inflammation and thus enhancing immunosenescence. This study aimed to assess whether excess body mass affected disproportion in T lymphocytes. Therefore, the study was designed to explain whether excess body mass in older individuals affected the disproportion in numbers of T lymphocytes and whether anthropometric indices and immune risk profile expressed as CD4/CD8 ratio are diagnostically useful in the analysis of immunosenescence.

MATERIALS & METHODS

One hundred three individuals aged 73.6 ± 3.1 years were allocated to the normal body mass (body mass index (BMI) 18.5-24.9 kg/m2,n = 39), the pre-obesity (BMI 25.0-29.9 kg/m2, n = 44) or the obesity (BMI ≥30.0 kg/m2, n = 20) group, based on WHO recommendations. Details on the subjects' medical history and lifestyle were obtained by health questionnaire. Anthropometric analysis was performed by bioelectrical impedance method, biochemical analysis was made by the automatic analyzer and ELISA immunoassays, and T and B lymphocyte counts were determined by eight-parameter flow cytometry. Additionally, visceral adiposity index, body adiposity index (BAI), and body shape index (ABSI) were evaluated based on body circumference, BMI and lipid-lipoprotein profile measurements.

RESULTS

The highest percentage of CD3+CD4+ T lymphocytes (59.4 ± 12.6 %) and the lowest CD3+CD8+ T lymphocytes (31.6 ± 10.0 %) were noted in patients the obesity group. The highest cut-off value of 1.9 for CD4/CD8 ratio was recorded in the normal body mass vs pre-obesity model. CD4/CD8 ratio > 2.5 was recorded in >20 % of our pre-obesity and obesity groups while 64.5 % of the normal body mass group had CD4/CD8 ratio < 1. High diagnostic usefulness was demonstrated for both BAI and lipid accumulation product (LAP) (AUC values of ~0.800 and ~ 0.900 respectively) in three models: normal body mass vs pre-obesity, normal body mass vs obesity, and pre-obesity vs obesity.

CONCLUSION

The odds ratios (OR) for CD4/CD8 ratio in the normal body mass vs obesity model (OR = 16.1, 95%CI 3.8-93.6) indicated a potential diagnostic value of T lymphocytes for clinical prognosis of immune aging in relation to excess body weight in older adults. High values of AUC obtained for the following models: CD4/CD8 + BAI (AUC = 0.927), CD4/CD8 + LAP (AUC = 1.00), CD4/CD8 + ABSI (AUC = 0.865) proved to provide excellent discrimination between older adults with obesity and with normal body mass.

Relationship between lipid accumulation product and new-onset diabetes in the Japanese population: a retrospective cohort study

Background

Diabetes has become a global public health problem. Obesity has been established as a risk factor for diabetes. However, it remains unclear which of the obesity indicators (BMI, WC, WhtR, ABSI, BRI, LAP, VAI) is more appropriate for monitoring diabetes. Therefore, the objective of this investigation is to compare the strength of the association of these indicators and diabetes and reveal the relationship between LAP and diabetes.

Methods

15,252 people took part in this research. LAP was quartered and COX proportional risk model was applied to explore the relationship between LAP and new-onset diabetes. Smooth curve fitting was employed to investigate the non-linear link between LAP and diabetes mellitus. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the aforementioned indicators for diabetes.

Results

After adjusting for confounding factors, multiple linear regression analysis showed that each unit increase in LAP was associated with a 76.8% increase in the risk of developing diabetes (HR=1.768, 95% CI: 1.139 to 2.746, P=0.011). In addition, LAP predicted new-onset diabetes better than other indicators, and the AUC was the largest [HR: 0.713, 95% CI: 0.6806-0.7454, P<0.001, in women; HR: 0.7922, 95% CI: 0.7396-0.8447; P<0.001, in men]. When LAP was used as a lone predictor, its AUC area was largest both men and women. However, after adding classical predictors (FPG, HbA1c, SBP, exercise, age) to the model, the LAP is better than the ABSI, but not better than the other indicators when compared in pairs.

Conclusions

High levels of LAP correlate very strongly with diabetes and are an important risk factor for diabetes, especially in women, those with fatty liver and current smokers. LAP was superior to other indicators when screening for diabetes susceptibility using a single indicator of obesity, both in men and in women. However, when obesity indicators were added to the model together with classical predictors, LAP did not show a significant advantage over other indicators, except ABSI.

Cardiometabolic indices predict hypogonadism in male patients with type 2 diabetes

PURPOSE

To evaluate in men with type 2 diabetes the association of cardiometabolic indices [Visceral Adiposity Index (VAI), Triglyceride Glucose Index (TyG), and lipid accumulation product (LAP)] with total testosterone (TT) levels, and their predictive cut-off values in identifying hypogonadism.

METHODS

265 consecutive men aged 40-70 years with type 2 diabetes performed an andrological evaluation; metabolic parameters and TT were determined. Receiver operating characteristic (ROC) curves were used to identify cut-off values of cardiometabolic indices in predicting low testosterone (TT < 12 nmol/l).

RESULTS

VAI, TyG, and LAP were negatively associated with TT levels. The prevalence of hypogonadism in men in the fourth quartiles of VAI, TyG, and LAP was ~ 70.0-75.0% compared to ~ 10.0-17.0% in men in the first quartiles (p < 0.001). The sensitivity and specificity of the three cardiometabolic indices in predicting TT < 12 nmol/l were significantly higher concerning BMI, waist circumference, lipid profile and HbA1c. Cut off values of VAI ≥ 3.985, TyG ≥ 4.925, and LAP ≥ 51.645 predict hypogonadism with good sensitivity and specificity.

CONCLUSION

This is the first study evaluating the association of VAI, TyG, and LAP with hypogonadism in men with type 2 diabetes. Alterations in these indices should direct the patients to andrological evaluation.

Assessment of metabolic syndrome predictors in relation to inflammation and visceral fat tissue in older adults

The diagnosis of metabolic syndrome (MetS) focuses on the assessment of risk factors such as insulin resistance, dyslipidemia, central adiposity and elevated blood pressure. Evidence suggests that markers of systemic inflammation may also be included in the definition of MetS and play some role in its pathogenesis. The study was designed to evaluate low-grade inflammation status in older adults with MetS in relation to increased body fat tissue and an attempt was made to evaluate new predictors for MetS through the analysis of the ROC Curve. Ninety-six middle-aged (69.2 ± 4.9) individuals from University of Third Age (women n = 75 and men n = 21) were allocated to two groups: without metabolic syndrome (n = 37) and with metabolic syndrome (n = 59) according to International Diabetes Federation criteria in agreement with American Heart Association/National Heart, Lung and Blood Institute 2009. Participants' current health status was assessed using medical records from a routine follow-up visit to a primary care physician. Statistical analysis was performed using R studio software. Depending on the normal distribution, ANOVA or the Kruskal-Wallis test was used. The optimal threshold value for clinical stratification (cut-off value) was obtained by calculating the Youden index. The AUC was observed to be the highest for a new anthropometric index i.e. lipid accumulation product (0.820). Low-grade inflammation dominated in MetS group (BMI 28.0 ± 4.4 kg/m², WHR 0.9 ± 0.1, FM 24.7 ± 7.9 kg) where significantly higher values of TNF-α (p = 0.027) and HGMB-1 protein (p = 0.011) were recorded.The optimal threshold values for immunological indices assessed as new predictors of the metabolic syndrome were: 93.4 for TNF-α, 88.2 for HGMB-1 protein and 1992.75 for ghrelin. High AUC values for these indices additionally confirmed their high diagnostic usefulness in MetS.

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

Objective

Microalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.

Methods

This cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.

Results

Systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.

Conclusion

Based on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.