Migrant Asian Indians in New Zealand; prediction of metabolic syndrome using body weights and measures

Identifying metabolic syndrome in migrant Asian Indian adults with anthropometric and visceral fat action points

BACKGROUND

Metabolic syndrome (MetS) is a clustering of metabolic risk factors, including large waist circumference (WC). Other anthropometric parameters and visceral fat mass (VFM) predicted from these may improve MetS detection. Our aim was to assess the ability of such parameters to predict this clustering in a cross-sectional, diagnostic study.

METHOD

Participants were 82 males and 86 females, aged 20-74 years, of Asian Indian ethnicity. VFM was estimated by dual-energy X-ray absorptiometry (DXA) through identification of abdominal subcutaneous fat layer boundaries. Non-anthropometric metabolic risk factors (triglycerides, HDL cholesterol, blood pressure and glucose) were defined using MetS criteria. We estimated the ability of anthropometry and VFM to detect ≥ 2 of these factors by receiver operating characteristic (ROC) and precision-recall curves.

RESULTS

Two or more non-anthropometric metabolic risk factors were present in 45 (55%) males and 29 (34%) females. The area under the ROC curve (AUC) to predict ≥ 2 of these factors using WC was 0.67 (95% confidence interval: 0.55-0.79) in males and 0.65 (0.53-0.77) in females. Optimal WC cut-points were 92 cm for males (63% accuracy) and 79 cm for females (53% accuracy). VFM, DXA-measured sagittal diameter and suprailiac skinfold thickness yielded higher AUC point estimates (by up to 0.06), especially in females where these measures improved accuracy to 69%, 69% and 65%, respectively. Pairwise combinations that included WC further improved accuracy.

CONCLUSION

Our findings indicate that cut-points for readily obtained measures other than WC, or in combination with WC, may provide improved detection of MetS risk factor clusters.

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study

Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.

Body Composition Methods in Adults with Type 2 Diabetes or at Risk for T2D: a Clinical Review

PURPOSE OF REVIEW

The aim of this study is to summarize anthropometric and advanced methods used to assess body composition in adults diagnosed with type 2 diabetes (T2D) or at risk for T2D that provide clinically relevant information about T2D disease-related complications or risk factors.

RECENT FINDINGS

Anthropometry is commonly used in clinical settings; however, provides unreliable estimates of fat mass, fat-free mass, and body fat distribution for metabolic health assessments compared to advanced techniques such as bioelectrical impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA), computerized tomography (CT), and magnetic resonance imaging (MRI). Few studies report the clinical use of anthropometric and advanced body composition methods that identify T2D disease-related complications or T2D risk factors. Anthropometry, BIA, DXA, CT, and MRI were used to estimate body adiposity and distribution, visceral and subcutaneous adipose tissue depots, and skeletal muscle mass. Review findings indicate that these methods were capable of identifying clinically relevant T2D disease-related complications such as sarcopenia and T2D risk factors such as obesity or regional adiposity. However, estimates were often sex and race/ethnicity specific warranting cross-validation of these methods in broader populations with T2D or risk for T2D prior to clinical implementation.

Associations Between Dietary Antioxidant Intake and Markers of Atherosclerosis in Middle-Aged Women From North-Western Algeria

Background: The role of several dietary antioxidants in preventing the development and the progression of atherosclerosis has recently aroused considerable interest. Although they are not yet conclusive, most of the existing suggestions support this hypothesis.

Objective: The aim of the present work was to investigate the intake of dietary antioxidant nutrients in relation to atherogenic indices in a group of Algerian middle aged women with and without type 2 diabetes.

Methods: A cross-sectional study was conducted on a group of middle-aged women from the north western region of Algeria. Anthropometric and biochemical parameters were measured. Dietary intake was assessed using a validated 3-days food record. Atherogenic indices -total cholesterol-to-high-density lipoprotein cholesterol ratio (TC/HDL) and apolipoprotein (apo) B-to-apo A1 ratio, were calculated. Associations between antioxidants dietary intake and atherogenic indices were examined using logistic regressions.

Results: 95 women with type 2 diabetes were compared to 93 non-diabetic ones. Statistical differences (p < 0.05) were revealed for body weight, height, body mass index (BMI), glycosylated hemoglobin (HbA1c) and total cholesterol levels. Furthermore, significant differences were noted for vitamin C, E and copper dietary intakes. The TC/HDL ratio was significantly associated to the highest quartiles of vitamin C in all patients; 3.519[2.405–4.408], p = 0.009 and in non-diabetic women; 3.984[1.775–7.412], p = 0.020, respectively. The odd ratios of vitamin E intakes were about 2.425[2.017–5.715], p = 0.012 in all patients and 1.843[1.877–2.731], p = 0.019 in non-diabetic group, respectively. However, the Apo B/Apo A1 ratio was more correlated to the highest quartiles of zinc and copper in non-diabetic group; OR = 0.059[0.006–0.572], p = 0.015 and 0.192[0.048–0.766], p = 0.019, respectively.

Conclusion: The estimated risk of atherosclerosis measured through the TC/HDL ratio was correlated to vitamins antioxidant intake, while the probable risk assessed by the Apo B/Apo A1 ratio was more associated to the mineral profile.

Comparison of waist to height ratio and body indices for prediction of metabolic disturbances in the Korean population: the Korean National Health and Nutrition Examination Survey 2008-2011

BACKGROUND

The aim of the present study of the general population was to identify the best predictor of metabolic risk among the body index variables evaluated with dual-energy X-ray absorptiometry (DEXA) or anthropometric indices including the waist to height ratio (WHtR).

PATIENTS AND METHODS

Data from the Korean National Health and Nutrition Examination Survey 2008-2011 were used for the analyses. As a result, 15,965 participants were included in this study. The body mass (BM) index was calculated as the body weight divided by the height squared. The WHtR was calculated as the waist circumference divided by height. Body composition indices such as lean mass (LM), fat mass (FM), trunk fat mass (TFM), and bone mineral content (BMC) were determined by using DEXA. Skeletal muscle mass (SM) was defined as the sum of the lean soft masses of both extremities. The LM, FM, BMC, TFM, and SM indices were calculated by dividing the total LM, total FM, total BMC, TFM, or SM by the height squared.

RESULTS

The WHtR had the highest area under the curve (AUC) and was the best predictor of metabolic syndrome for both sexes. In addition, the WHtR had the highest AUCs for components of metabolic syndrome (male: AUC 0.823, 95 % confidence interval [CI] 0.814-0.832; female: AUC 0.870, 95 % CI 0.863-0.877). There was a small statistically significant difference in AUC between WHtR and the other indices. Multivariate logistic regression showed that male participants in the second, third, and fourth quartiles had a 4.0 (95 % CI, 3.1-5.2), 9.6 (95 % CI, 7.5-12.3), and 36.1 (95 % CI, 28.0-46.4) times increased risk of metabolic syndrome compared with patients in the first quartile and female participants in the second, third, and fourth quartiles had a 4.3 (95 % CI, 3.1-6.0), 18.0 (95 % CI, 13.3-24.5), and 58.5 (95 % CI, 42.9-79.9) times increased risk of metabolic syndrome compared with patients in the first quartile.

CONCLUSION

Among the BM, FM, LM, SM, TFM, and WHtR indices, WHtR is most useful to predict the presence of metabolic syndrome and insulin resistance in the Korean population.