Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec 15:10:1026751.
doi: 10.3389/fpubh.2022.1026751. eCollection 2022.

Transition patterns of metabolism-weight phenotypes over time: A longitudinal study using the multistate Markov model in China

Affiliations

Transition patterns of metabolism-weight phenotypes over time: A longitudinal study using the multistate Markov model in China

Hongya Zhang et al. Front Public Health. .

Abstract

Background: A change in weight or metabolic status is a dynamic process, yet most studies have focused on metabolically healthy obesity (MHO) and the transition between MHO and metabolically unhealthy obesity (MUO); therefore, they have not fully revealed the nature of all possible transitions among metabolism-weight phenotypes over the years.

Methods: This was a longitudinal study based on a retrospective health check-up cohort. A total of 9,742 apparently healthy individuals aged 20-60 years at study entry were included and underwent at least two health check-ups. Six metabolism-weight phenotypes were cross-defined by body mass index (BMI) categories and metabolic status as follows: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), MHO, metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and MUO. A multistate Markov model was used to analyse all possible transitions among these phenotypes and assess the effects of demographic and blood indicators on the transitions.

Results: The transition intensity from MUNW to MHNW was the highest (0.64), followed by the transition from MHO to MUO (0.56). The greatest sojourn time appeared in the MHNW state (3.84 years), followed by the MUO state (2.34 years), and the shortest sojourn time appeared in the MHO state (1.16 years). Transition intensities for metabolic improvement gradually decreased with BMI level as follows: 0.64 for MUNW to MHNW, 0.44 for MUOW to MHNW, and 0.27 for MUO to MHO; however, transition intensities for metabolic deterioration, including MHNW to MUNW, MHOW to MUOW, and MHO to MUO, were 0.15, 0.38, and 0.56, respectively. In the middle-aged male group, elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and uric acid (UA) increased the risk of deterioration in weight and metabolic status and decreased the possibility of improvement.

Conclusion: Maintaining a normal and stable BMI is important for metabolic health. More attention should be given to males and elderly people to prevent their progression to an unhealthy metabolic and/or weight status. MHO is the most unstable phenotype and is prone to convert to the MUO state, and individuals with abnormal ALT, AST and UA are at an increased risk of transitioning to an unhealthy weight and/or metabolic status; therefore, we should be alert to abnormal indicators and MHO. Intervention measures should be taken early to maintain healthy weight and metabolic status.

Keywords: China; longitudinal study; metabolic status; multistate Markov model; obesity.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A six-state Markov model was used to describe the transition patterns among different metabolism-weight phenotypes, which were metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obesity (MUO).
Figure 2
Figure 2
Transition intensity from one phenotype to another estimated by the multistate Markov model. MHNW, metabolically healthy normal weight; MHOW, metabolically healthy overweight; MHO, metabolically healthy obesity; MUNW, metabolically unhealthy normal weight; MUOW, metabolically unhealthy overweight; MUO, metabolically unhealthy obesity.
Figure 3
Figure 3
Predicted probabilities of transition from MHNW, MHOW, MHO, MUNW, MUOW, MUO to any other state over 6 years. MHNW, metabolically healthy normal weight; MHOW, metabolically healthy overweight; MHO, metabolically healthy obesity; MUNW, metabolically unhealthy normal weight; MUOW, metabolically unhealthy overweight; MUO, metabolically unhealthy obesity.
Figure 4
Figure 4
Factors showing the significant effects on transitions from one phenotype to another in a multiple variable analysis. Significant factors included sex, age, ALT, AST and UA. Sex, females vs. males; Age, middle-aged group (>45 years old) vs. young group (≤ 45 years old); ALT, elevated vs. normal; AST, elevated vs. normal; UA, elevated vs. normal. The direction of the arrow beside a factor denotes the significant impact of this factor on a certain transition. ↑means the factor increases the probability of the transition compared with the reference level; ↓means the factor decreases the probability of the transition compared with the reference level. ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid.
Figure 5
Figure 5
Assessment plots of the multistate Markov model showing the observed and expected percentages of each phenotype against time. MHNW, metabolically healthy normal weight; MHOW, metabolically healthy overweight; MHO, metabolically healthy obesity; MUNW, metabolically unhealthy normal weight; MUOW, metabolically unhealthy overweight; MUO, metabolically unhealthy obesity.

Similar articles

Cited by

References

    1. Lyall DM, Celis-Morales C, Ward J, Iliodromiti S, Anderson JJ, Gill JM, et al. . Association of body mass index with cardiometabolic disease in the uk biobank: a mendelian randomization study. JAMA Cardiol. (2017) 2:882–9. 10.1001/jamacardio.2016.5804 - DOI - PMC - PubMed
    1. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. (2006) 444:840–6. 10.1038/nature05482 - DOI - PubMed
    1. Bosello O, Donataccio MP, Cuzzolaro M. Obesity or obesities? Controversies on the association between body mass index and premature mortality. Eat Weight Disord. (2016) 21:165–74. 10.1007/s40519-016-0278-4 - DOI - PubMed
    1. Bhaskaran K, Douglas I, Forbes H. dos-Santos-Silva I, Leon DA, Smeeth L, et al. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults. Lancet. (2014) 384:755–65. 10.1016/S0140-6736(14)60892-8 - DOI - PMC - PubMed
    1. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. . The global obesity pandemic: shaped by global drivers and local environments. Lancet. (2011) 378:804–14. 10.1016/S0140-6736(11)60813-1 - DOI - PubMed

LinkOut - more resources