Metabolic Score for Visceral Fat (METS-VF), a novel estimator of intra-abdominal fat content and cardio-metabolic health

Bello‐Chavolla, Omar Yaxmehen; Antonio-Villa, Neftalí Eduardo; Vargas‐Vázquez, Arsenio; Viveros-Ruiz, Tannia Leticia; Almeda‐Valdés, Paloma; Gómez‐Velasco, Donají; Mehta, Roopa; Elías-López, Daniel; Cruz-Bautista, Ivette; Roldan-Valadez, Ernesto; Martagón, Alexandro J.; Aguilar-Salinas, Carlos A.

doi:10.1016/j.clnu.2019.07.012

Cited by 65 publications

(75 citation statements)

References 29 publications

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“…Model 4: Replacing HOMA for METS-IR (a non-insulin-based model for IR, metabolic score for IR), METS-VF (a visceral fat estimator, metabolic score for visceral fat), 13 14 HbA1c, BMI and age at diabetes onset.…”

Section: Methodsmentioning

confidence: 99%

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Bello‐Chavolla

Bahena-López

Vargas‐Vázquez

et al. 2020

BMJ Open Diab Res Care

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IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.

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Section: Methodsmentioning

confidence: 99%

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Bello‐Chavolla

Bahena-López

Vargas‐Vázquez

et al. 2020

BMJ Open Diab Res Care

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“…Water is relevant, because it has different increases depending on the trimester of pregnancy, with an estimated growth at the end of 5–8 litres (Lederman et al., 1997). MRI can depict structural changes in the kidney (Dallmann et al., 2017), pancreas (Ernst et al., 2011), and intra‐abdominal fat content (Bello‐Chavolla et al., 2020), these structures participate in body composition changes during all trimesters of pregnancy. MRI (Takahashi et al., 2014) together with ADP (Bosaeus et al., 2015) depicted significant growing trends.…”

Section: Discussionmentioning

confidence: 99%

Quantitative systematic review: Methods used for the in vivo measurement of body composition in pregnancy

Guzmán-Ortiz

Bueno‐Hernández

Meléndez-Mier³

et al. 2020

Journal of Advanced Nursing

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“…al. 30 METS-VF was calculated using by the following equation: We defined as visceral obese, subjects with METS-VF > 7.18 in NHANES and ENSANUT-cohorts. In the SIGMA-cohort, we performed DXA VAT assessments according to the standardized methods described in the Supplementary Material.…”

Section: Methodsmentioning

confidence: 99%

Elevated serum uric acid is a facilitating mechanism for insulin resistance mediated accumulation of visceral adipose tissue

Fernández‐Chirino

Antonio-Villa

Vargas‐Vázquez

et al. 2020

Preprint

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BACKGROUND: Serum uric acid (SUA) has a relationship with cardiometabolic conditions such as insulin resistance (IR) and visceral adipose tissue (VAT) accumulation. Here, we aimed to clarify the nature of this relationship and the underlying causality mechanism. METHODS: We conducted a population-based cross-sectional study comprising 8,504 subjects joining both NHANES 2003-2004 and 2011-2012 cycles and ENSANUT Medio Camino 2016. We performed mixed effects linear regression models using HOMA2-IR, adipoIR, and METS-VF as indicators of IR and VAT accumulation. Furthermore, we performed mediation analyses to assess a potential causal mechanism and ROC curves to establish cut-off points for identification of IR and visceral obesity using SUA. Finally, with an additional dataset comprised of 226 subjects with both euglycemic hyperinsulinemic clamp (EHC) and dual X-ray absorptiometry (DXA) measurements for IR and VAT accumulation, we performed a network of confirmatory mediation analyses. RESULTS:We found that SUA has a mediating role inside the bidirectional relationship between IR and visceral obesity, and it is part of an underlying causality mechanism which includes adiponectin. The proportion of the mechanism mediated by SUA is greater when stated that IR (in either peripheral or adipose tissue) leads to VAT accumulation (14.90%[13.20%-17.00%] and 15.54%[13.61% - 18.00%] to 4.88%[3.06%-7.00%] and 8.13%[5.91% - 10.00%]) instead of the opposite direction. This result was confirmed by mediation analyses using gold-standard measurements. CONCLUSIONS:Elevated SUA acts as mediator inside the bidirectional relationship between IR andVAT accumulation. Its role appears to be larger when considering adipose tissue IR as the promoter for VAT accumulation.

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Metabolic Score for Visceral Fat (METS-VF), a novel estimator of intra-abdominal fat content and cardio-metabolic health

Cited by 65 publications

References 29 publications

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Quantitative systematic review: Methods used for the in vivo measurement of body composition in pregnancy

Elevated serum uric acid is a facilitating mechanism for insulin resistance mediated accumulation of visceral adipose tissue

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