Changes in Metabolic Health Status Over Time and Risk of Developing Type 2 Diabetes

Lee, Seung Hwan; Yang, Hae Kyung; Ha, Hee‐Sung; Lee, Jin‐Hee; Kwon, Hyuk‐Sang; Park, Young Jun; Yim, Haejun; Kang, Min-Jeoung; Lee, Won‐Chul; Son, Ho‐Young; Yoon, Kun‐Ho

doi:10.1097/md.0000000000001705

Cited by 48 publications

(59 citation statements)

References 47 publications

(47 reference statements)

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“…Cohort studies found higher CVD risk for MHO subgroups with longer duration of follow-up, indicating a transient nature for the phenotype [8,10]. This has been confirmed by studies with follow-up of up to 10 years, the majority of which suggest that between one-third and one-half of individuals with MHO convert to an unhealthy phenotype [13,[28][29][30][31][32][33][34][35][36][37] (Table 2). Very few studies have been conducted over longer time periods.…”

Section: Long-term Trajectories Of Metabolic Healthmentioning

confidence: 63%

Metabolic health in normal-weight and obese individuals

Schulze

2018

Diabetologia

122

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Cardiovascular complications are commonly associated with obesity. However, a subgroup of obese individuals may not be at an increased risk for cardiovascular complications; these individuals are said to have metabolically healthy obesity (MHO). In contrast, metabolically unhealthy individuals are at high risk of cardiovascular disease (CVD), irrespective of BMI; thus, this group can include individuals within the normal weight category (BMI 18.5-24.9 kg/m 2 ). This review provides a summary of prospective studies on MHO and metabolically unhealthy normal-weight (MUHNW) phenotypes. Notably, there is ongoing dispute surrounding the concept of MHO, including the lack of a uniform definition and the potentially transient nature of metabolic health status. This review highlights the relevance of alternative measures of body fatness, specifically measures of fat distribution, for determining MHO and MUHNW. It also highlights alternative approaches of risk stratification, which account for the continuum of risk in relation to CVD, which is observable for most risk factors. Moreover, studies evaluating the transition from metabolically healthy to unhealthy phenotypes and potential determinants for such conversions are discussed. Finally, the review proposes several strategies for the use of epidemiological research to further inform the current debate on metabolic health and its determination across different stages of body fatness.

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Section: Long-term Trajectories Of Metabolic Healthmentioning

confidence: 63%

Metabolic health in normal-weight and obese individuals

Schulze

2018

Diabetologia

122

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“…Lee et al . reported that individuals in the highest baseline quartile of TyG index had a fourfold higher risk of developing diabetes in a long‐term Korean cohort, and recently, they showed that changes in the TyG index over time altered the incidence and risk of diabetes . It has also proposed as a candidate marker for classifying metabolic health status .…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, another research project found that the TyG index was the best index in discriminating IR individuals, even compared with visceral adiposity indicators and other lipid parameters [9]. Lee et al [29] reported that individuals in the highest baseline quartile of TyG index had a fourfold higher risk of developing diabetes in a long-term Korean cohort, and recently, they showed that changes in the TyG index over time altered the incidence and risk of diabetes [30]. It has also proposed as a candidate marker for classifying metabolic health status [31].…”

Section: Research In Contextmentioning

confidence: 99%

The TyG index may predict the development of cardiovascular events

Sánchez-Íñigo¹,

Navarro‐González²,

Fernandez‐Montero

et al. 2016

Eur J Clin Investigation

334

281

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“…Additionally, the index can better predict the patient's metabolic status, as it has been shown in discriminating atypical metabolic phenotypes, such as metabolically obese normal weight and healthy obese, as well as their progression throughout time 35 . Cutoff points have been determined to discriminate these pathological metabolic status in two populations of Korea 34, 35 , which could facilitate the definition of these phenotypes.…”

Section: Discussionmentioning

confidence: 99%

“…Cutoff points have been determined to discriminate these pathological metabolic status in two populations of Korea 34, 35 , which could facilitate the definition of these phenotypes. Future studies in our population should evaluate the utility of this index in the identification of these abnormal pathological statuses.…”

Section: Discussionmentioning

confidence: 99%

Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population

et al. 2017

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Background: Insulin resistance (IR) evaluation is a fundamental goal in clinical and epidemiological research. However, the most widely used methods are difficult to apply to populations with low incomes. The triglyceride-glucose index (TGI) emerges as an alternative to use in daily clinical practice. Therefore the objective of this study was to determine an optimal cutoff point for the TGI in an adult population from Maracaibo, Venezuela. Methods: This is a sub-study of Maracaibo City Metabolic Syndrome Prevalence Study, a descriptive, cross-sectional study with random and multi-stage sampling. For this analysis, 2004 individuals of both genders ≥18 years old with basal insulin determination and triglycerides < 500 mg/dl were evaluated.. A reference population was selected according to clinical and metabolic criteria to plot ROC Curves specific for gender and age groups to determine the optimal cutoff point according to sensitivity and specificity.The TGI was calculated according to the equation: ln [Fasting triglyceride (mg / dl) x Fasting glucose (mg / dl)] / 2. Results: The TGI in the general population was 4.6±0.3 (male: 4.66±0.34 vs. female: 4.56±0.33, p=8.93x10 -10). The optimal cutoff point was 4.49, with a sensitivity of 82.6% and specificity of 82.1% (AUC=0.889, 95% CI: 0.854-0.924). There were no significant differences in the predictive capacity of the index when evaluated according to gender and age groups. Those individuals with TGI≥4.5 had higher HOMA2-IR averages than those with TGI <4.5 (2.48 vs 1.74, respectively, p<0.001). Conclusions: The TGI is a measure of interest to identify IR in the general population. We propose a single cutoff point of 4.5 to classify individuals with IR. Future studies should evaluate the predictive capacity of this index to determine atypical metabolic phenotypes, type 2 diabetes mellitus and even cardiovascular risk in our population.

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Changes in Metabolic Health Status Over Time and Risk of Developing Type 2 Diabetes

Abstract: Supplemental Digital Content is available in the text

Cited by 48 publications

References 47 publications

Metabolic health in normal-weight and obese individuals

Metabolic health in normal-weight and obese individuals

The TyG index may predict the development of cardiovascular events

Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population

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