Identification of an obesity index for predicting metabolic syndrome by gender: the rural Chinese cohort study

Diagnostic Accuracy of Waist-to-Height Ratio, Waist Circumference, and Body Mass Index in Identifying Metabolic Syndrome and Its Components in Older Adults: A Systematic Review and Meta-Analysis

Background

Although numerous studies have indicated the utility of waist-to-height ratio (WHtR) in early screening for individuals with adverse cardiometabolic health, there is controversy on using WHtR as a one-size-fits-all approach, including in older adults.

Objectives

Our study aims to identify the pooled diagnostic accuracy of WHtR in screening for metabolic syndrome (MetS) and its components among older adults.

Methods

A systematic review of observational studies was performed using 4 databases. A diagnostic meta-analysis with a random effects model was conducted, and the pooled area under the summary receiver operating characteristic curve, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (dOR) of each outcome compared with WHtR, body mass index (BMI), and waist circumference (WC) were calculated, with sex-stratified analysis.

Results

A total of 17 studies with 74,520 participants were included. As reflected by the dOR, WHtR (7.65; 95% CI: 6.00, 9.75) performed better than BMI (5.17; 95% CI: 4.75, 5.62) and WC (5.77; 95% CI: 4.60, 7.25) in screening for MetS among older adults and was potentially better among males. For hyperglycemia, hypertension, and dyslipidemia, the performances of WHtR, BMI, and WC were comparable.

Conclusion

More studies focusing on older adults are still needed to determine the cutoff values of WHtR to screen for MetS.The search strategy was registered in PROSPERO as CRD42022350379.

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.

Diagnostic performance using obesity and lipid-related indices and atherogenic index of plasma to predict metabolic syndrome in the adult sudanese population

Background

Simple and accurate clinical indicators to detect metabolic abnormalities might be helpful for early management and lowering the risk of future consequences like cardiovascular disease and type 2 diabetes mellitus.

Aim

The visceral adiposity index (VAI), lipid accumulation product (LAP), and atherogenic index of plasma (AIP) have been proposed as reliable, straightforward clinical markers and indications of metabolic syndrome (MetS). This study aimed to see how well these obesity and lipid-related indicators will predict MetS in adult Sudanese patients.

Subjects and Methods

This community hospital-based case-control study included 420 middle-aged people (154 men and 266 women). Anthropometric measurements, weight (kilogram), height (meters), and waist circumference (WC) were evaluated, and the body mass index (BMI) and waist-to-height ratio (WHtR) were calculated. Fasting blood samples were collected for glycated hemoglobin (HbA1c) and lipid profile assessment. VAI, LAP, and AIP were calculated.

Results

Significantly higher means of BMI, WC, WHtR, HbA1c, triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides/high-density lipoprotein cholesterol (TG/HDL-C) ratio, LAP, VAI, AIP, and significantly decrease in high-density lipoprotein cholesterol (HDL-C) were seen among MetS when compared with non-MetS group. LAP had a significant proportion with BMI, WC, WHtR, TG, TG/HDL-C, VAI, and AIP, and it is inversely related to HDL-C in the MetS group. On ROC analysis, LAP had the largest operating characteristic curves (AUC) for both gender 0.970 (0.948-0.993) for men and 0.964 (0.945-0.982) for women, followed by WC, and VAI, while BMI showed the lowest AUCs for men and women. In multiple regression analyses, AIP values increased significantly with LDL-C, DBP, HbA1c, LAP, and VAI.

Conclusion

The LAP was considerably higher in middle-aged people with MetS in both gender and was considered the best diagnostic performance.

Optimal Anthropometric Indicators and Cut Points for Predicting Metabolic Syndrome in Chinese Patients with Type 2 Diabetes Mellitus by Gender

PURPOSE

The best predictors and cut points for metabolic syndrome (MetS) in Chinese patients with type 2 diabetes (T2DM) were determined by comparing six anthropometric measures: body mass index (BMI), triglyceride-glucose (TyG), the product of TyG and waist-to-hip ratio (TyG-WHpR), the product of TyG and waist-to-height ratio (TyG-WHtR), the product of TyG and waist circumference (TyG-WC), and the product of TyG and body mass index (TyG-BMI).

PATIENTS AND METHODS

Sixteen hundred and sixty-five adult patients with T2DM were collected, and the ability and cut points of each index to predict MetS were compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) values. Then, logistic regression analysis was used to adjust for confounders, including adjustment for menopause in women, to obtain the odds ratio (OR) and 95% confidence interval (CI).

RESULTS

MetS was present in 71.60% of T2DM patients, 75.00% of men, and 67.02% of women. BMI was the best predictor of MetS in men with T2DM (AUC = 0.8646, 95% CI: 0.8379-0.8912), with a cut point of 24.5500 kg/m² (specificity: 0.7714; sensitivity: 0.7533), and TyG-WC was the best predictor of MetS in women with T2DM (AUC = 0.8362, 95% CI: 0.8034-0.8690), with a cut point of 154.1548 (specificity: 0.7455; sensitivity: 0.8076).

CONCLUSION

The best predictor of MetS in adults with T2DM is BMI with a cut point of 24.5500 kg/m² for men and TyG-WC with a cut point of 154.1548 for women.

Assessing metabolic syndrome prediction quality using seven anthropometric indices among Jordanian adults: a cross-sectional study

Metabolic syndrome (MSyn) is a considerable health concern in developing and developed countries, and it is a critical predictor of all-cause mortality. Obesity, specifically central obesity, is highly associated with MSyn incidence and development. In this study, seven anthropometric indices (Body Mass Index (BMI), Waist circumference (WC), Waist-to-Height Ratio (WHtR), A Body Shape Index (ABSI), Body Roundness Index (BRI), conicity index (CI), and the Visceral Adiposity Index (VAI)) were used to identify individuals with MSyn among the Jordanian population. These indices were assessed to identify their superiority in predicting the risk of MSyn. A total of 756 subjects (410 were male and 346 were female) were met between May 2018 and September 2019 and enrolled in this study. Height, weight, and waist circumferences were measured and BMI, WHtR, ABSI, BRI, CI, and VAI were calculated. Fasting plasma glucose level, lipid profile, and blood pressure were measured. Receiver-operating characteristic (ROC) curve was used to determine the discriminatory power of the anthropometric indices as classifiers for MSyn presence using the Third Adult Treatment Panel III (ATP III) definition. MSyn prevalence was 42.5%, and obese women and men have a significantly higher prevalence. BRI and WHtR showed the highest ability to predict MSyn (AUC = 0.83 for both indices). The optimal cutoff point for an early diagnosis of MSyn was > 28.4 kg/m² for BMI, > 98.5 cm for WC, > 5.13 for BRI, > 0.09 m^11/6 kg^-2/3 for ABSI, > 5.55 cm² for AVI, > 1.33 m^3/2 kg^-1/2 for CI, and > 0.59 for WHtR with males having higher cutoff points for MSyn early detection than females. In conclusion, we found that WHtR and BRI may be the best-suggested indices for MSyn prediction among Jordanian adults. These indices are affordable and might result in better early detection for MSyn and thereby may be helpful in the prevention of MSyn and its complications.

Anthropometric Cut-Off Values for Detecting the Presence of Metabolic Syndrome and Its Multiple Components among Adults in Vietnam: The Role of Novel Indices

Recent studies have shown that using international guidelines to diagnose metabolic syndrome (MetS) may underestimate its prevalence in different Asian populations. This study aims to determine the validity of anthropometric indicators and appropriate cut-off values to predict MetS for Vietnamese adults. We analyzed data on 4701 adults across four regions of Vietnam. Four conventional and five novel anthropometric indexes were calculated. The area under a receiver operating characteristic (ROC) curve (AUC) and Youden’s J statistic were applied to evaluate the diagnostic ability and optimal cut-off values. Regardless of diagnostic criteria and gender, Abdominal volume index (AVI), Body roundness index (BRI), and Waist-height ratio (WHtR) had the highest AUC values, followed by Body mass index (BMI) and Waist-hip ratio (WHR). However, it was seen that differences among the AUC values of most indices were minor. In men, using International Diabetes Federation (IDF) criteria, the threshold of indices was 3.86 for BRI, 16.20 for AVI, 0.53 for WHtR, 22.40 for BMI, and 0.90 for WHR. In women, the threshold for these figures were 3.60, 12.80, 0.51, 23.58, and 0.85, respectively. It is recommended that health personnel in Vietnam should apply appropriate thresholds of anthropometry, which are lower than current international guidelines, for MetS screening to avoid under-diagnosis.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

OBJECTIVE

The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

METHODS

The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model.

RESULTS

Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase.

CONCLUSION

The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

Association of Lipid Accumulation Product and Triglyceride-Glucose Index with Metabolic Syndrome in Young Adults: A Cross-sectional Study

BACKGROUND

Metabolic syndrome is a cluster of elements linked with type 2 diabetes mellitus and cardiovascular disease (CVD). The early detection of individuals at the risk of developing metabolic syndrome can prevent the development of type 2 diabetes mellitus and CVD.

OBJECTIVES

This study aimed to evaluate the association of the lipid accumulation product (LAP) and triglyceride-glucose (TyG) index with metabolic syndrome among young adults.

METHODS

This cross-sectional study included 300 young adults within the age range of 20 - 40 years. Metabolic syndrome was defined according to modified National Cholesterol Education Program Adult Treatment Panel III guidelines. The LAP and TyG index were calculated. Multivariate logistic regression and receiver operating characteristic curve analyses were performed to assess the association of the LAP and TyG index with metabolic syndrome.

RESULTS

The LAP and TyG index were significantly associated with metabolic syndrome (P < 0.05). The LAP showed the highest area under the curve (0.882 and 0.905 in male and female subjects, respectively), followed by the TyG index (0.875 and 0.886 in male and female subjects, respectively, at P < 0.0001. The cut-off values for the LAP were 45.65 in males with a sensitivity and specificity of 80% and 46.91 in females with a sensitivity and specificity of 88%. The cut-off points for the TyG index were 8.63 in males with 80% sensitivity and 78.9% specificity and 8.54 in females with 83.3% sensitivity and 79.6% specificity.

CONCLUSIONS

The LAP and TyG index are significantly associated with metabolic syndrome in young adults. As simple and inexpensive markers, they can be used to identify individuals with metabolic syndrome with high sensitivity and specificity.

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

Background

We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS.

Methods

This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model.

Results

Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise.

Conclusions

Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.

Children's Lipid Accumulation Product Combining Visceral Adiposity Index is a Novel Indicator for Predicting Unhealthy Metabolic Phenotype Among Chinese Children and Adolescents

PURPOSE

The predictive capacity between children's lipid accumulation product (CLAP) combining visceral adiposity index (VAI), CLAP, and VAI with metabolically unhealthy phenotype remained unclear. This study aimed to compare the ability of CLAP combining VAI, CLAP, VAI and traditional adiposity indicators (body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR)) to predict metabolically unhealthy phenotype among Chinese children and adolescents.

PATIENTS AND METHODS

In the cross-sectional study, 1714 children and adolescents aged 12 to 18 were selected by random cluster sampling, underwent a questionnaire survey, physical examination, biochemical tests and body composition was measured by bioelectrical impedance analysis (BIA). Participants were divided into four phenotypes according to BMI and metabolic syndrome components. The logarithmic CLAP (LnCLAP), VAI, BMI, WC, WHtR and WHR were standardized for sex and age using the z-score method (standardized variables: LnCLAP-z, VAI-z, BMI-z, WC-z, WHtR-z and WHR-z).

RESULTS

LnCLAP-z ≥ 1, VAI-z ≥ 1, WC-z ≥ 1, and WHR-z ≥ 1 increased the risk of metabolically unhealthy normal-weight phenotype (the OR and 95% CI were 4.18 (1.75-10.02), 24.05 (12.79-45.21), 6.17 (1.14-33.51), 2.69 (1.07-6.72), respectively), LnCLAP-z ≥ 1, VAI-z ≥ 1 and WC-z ≥ 1 increased the risk of metabolically unhealthy overweight or obese phenotype (the OR and 95% CI were 2.67 (1.40-5.09), 10.30 (3.03-35.03), 2.19 (1.18-4.09), respectively). The area under the ROC curve (AUC) for CLAP combining VAI in the prediction of the metabolically unhealthy phenotype were 0.837 (0.776-0.899) and 0.876 (0.834-0.918) for boys and girls with normal-weight, 0.853 (0.803-0.903) and 0.794 (0.711-0.878) for boys and girls with overweight and obese (all P < 0.001), which were higher than CLAP, VAI, BMI, WC, WHtR and WHR.

CONCLUSION

Among Chinese children and adolescents, CLAP combining VAI was a more effective indicator than CLAP, VAI and traditional adiposity indicators in predicting unhealthy metabolic phenotype.

Sex Difference in the Associations among Obesity-Related Indices with Metabolic Syndrome in Patients with Type 2 Diabetes Mellitus

Background: The aim of this study was to investigate the associations among obesity-related indices and MetS in diabetic patients, and explore sex differences in these associations. Methods: Patients with type 2 DM were included from two hospitals in southern Taiwan. The Adult Treatment Panel III criteria for an Asian population were used to define MetS. In addition, the following obesity-related indices were evaluated: waist-to-height ratio, waist-hip ratio (WHR), conicity index (CI), body mass index (BMI), body roundness index, body adiposity index, lipid accumulation product (LAP), abdominal volume index, visceral adiposity index (VAI), abdominal volume index and triglyceride-glucose index. Results: A total of 1,872 patients with type 2 DM (mean age 64.0 ± 11.3 years, 808 males and 1,064 females) were enrolled. The prevalence rates of MetS were 59.8% and 76.4% in the males and female (p < 0.001), respectively. All of the obesity-related indices were associated with MetS in both sex (all p < 0.001). LAP and BMI had the greatest areas under the receiver operating characteristic curves in both sex. In addition, the interactions between BMI and sex (p = 0.036), WHR and sex (p = 0.016), and CI and sex (p = 0.026) on MetS were statistically significant. Conclusions: In conclusion, this study demonstrated significant relationships between obesity-related indices and MetS among patients with type 2 DM. LAP and VAI were powerful predictors in both sex. The associations of BMI, WHR and CI on MetS were more significant in the men than in the women.

The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics

This study investigated the diagnostic accuracy of using an artificial neural network (ANN) for the prediction of metabolic syndrome (MetS) based on socioeconomic status and lifestyle factors. The data of 27,415 subjects who went through examinations and answered questionnaires during three stages from 2006 to 2014 at a health institute in Taiwan were collected and analyzed. The repeated measurements over time were set as predictive factors and used to train and test an ANN for MetS prediction. Among the subjects, 18.3%, 24.6%, and 30.1% were diagnosed with MetS during the respective three stages. ANN analysis applied with an over-sampling technique performed with an area under the curve (AUC) of up to 0.93 based on different models. The over-sampling technique helped improve prediction performance in terms of sensitivity and F₂ measures. The results indicated that waist circumference, socioeconomic status (SES), and lifestyle factors can be utilized in a non-invasive screening tool to assist health workers in making primary care decisions when MetS is suspected. By predicting the occurrence of MetS, individuals or healthcare professionals can then develop preventive strategies in time, thus enhancing the effectiveness of health promotion.

Occupational Assessments of Risk Factors for Cardiovascular Diseases in Labors: An Application of Metabolic Syndrome Scoring Index

Unlike a traditional diagnosis of metabolic syndrome (MS), a numerical MS index can present individual fluctuations of health status over time. This study aimed to explore its value in the application of occupational health. Using a database of physiological and biochemical tests and questionnaires, data were collected from 7232 participants aged 20 to 64 years who received occupational health screenings at a health screening institution in 2018. Using confirmatory factor analysis, five components of MS were used to design an MS severity scoring index, which was then used to evaluate the risks of occupation factors. Waist circumference was the largest loading factor compared with the other MS components. Participants who worked in the traditional industrial, food processing, or electronic technology industries had higher MS severity than those in the logistics industry. Those who worked as a manager or over five years had a relatively high severity. The research showed that assessments based on an MS severity score are applicable when the risk factors of suboptimal health are involved. By monitoring the scores over time, healthcare professionals can propose preventive strategies in time, thus enhancing the effectiveness of occupational health examination services.

Association of weight-adjusted-waist index with incident hypertension: The Rural Chinese Cohort Study

AIMS

To explore the association between WWI and the incidence of HTN in the Rural Chinese Cohort Study.

METHODS AND RESULTS

We examined data for 10,338 non-hypertensive participants (39.49% men) aged ≥ 18 years from the Rural Chinese Cohort Study who completed a baseline examination during 2007-2008 and follow-up during 2013-2014. WWI was calculated as waist circumference (cm) divided by the square root of weight (kg). Multiple logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the probability of HTN across four WWI categories. Restricted cubic splines analysis was used to model the dose-response association of WWI and HTN. A total of 2078 participants had HTN during a median follow-up of 6 years. After adjusting for potential confounders, as compared with the lowest WWI category (<9.94 cm/√kg), with WWI 9.94 to 10.42, 10.42 to 10.91 and ≥ 10.91 cm/√kg, the ORs (95% CIs) for HTN were 1.12 (0.93-1.35), 1.40 (1.17-1.69) and 1.50 (1.24-1.82), respectively. Results of the sensitivity analyses were robust. The ORs were generally consistent on subgroup analysis by sex, smoking status, systolic blood pressure and diastolic blood pressure. Multiple logistic regression models with restricted cubic splines showed a non-linear positive association between WWI and HTN (P_nonlinearity < 0.001).

CONCLUSION

The highest WWI category was significantly associated with increased risk of HTN. Our findings may facilitate the development and promotion of obesity prevention strategies aimed at reducing the risk of HTN and provide evidence for healthcare policy in rural China.