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. 2024 Mar 4;14(1):5244.
doi: 10.1038/s41598-024-56061-x.

Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions

Shayesteh Khalili #  1 , Seyed Amir Ahmad Safavi-Naini #  2 , Paniz Zarand  3 , Safdar Masoumi  3 , Yeganeh Farsi  1 , Farhad Hosseinpanah  4 , Fereidoun Azizi  3
Affiliations

Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions

Shayesteh Khalili et al. Sci Rep. .

Abstract

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42-1.87), followed by the MU-NO group (HR 1.33-1.67), and the MH-O group (HR 1.18-1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Patient flow diagram of patients and individuals included in the final analysis.
Figure 2
Figure 2
Cumulative risk curve of CKD incident among four phenotypes of MH-NO Metabolically Healthy, No Obesity; MU-NO Metabolically Unhealthy, No Obesity; MH-O Metabolically Healthy, with Obesity; MU-O Metabolically Unhealthy with Obesity among (a) male and (b) female.
Figure 3
Figure 3
Age-sex adjusted risk of chronic kidney disease according to the transition of Obesity metabolic phenotype in twenty years follow-up. MH-NO Metabolically Healthy, No Obesity; MU-NO Metabolically Unhealthy, No Obesity; MH-O Metabolically Healthy, with Obesity; MU-O Metabolically Unhealthy with Obesity, 95 CI 95% confidence interval.
Figure 4
Figure 4
Cumulative risk curve of CKD incident state transitions among four phenotypes of: (a) MH-NO: Metabolically Healthy, No Obesity; (b) MU-NO: Metabolically Unhealthy, No Obesity; (c) MH-O: Metabolically Healthy, with Obesity; (d) MU-O: Metabolically Unhealthy with Obesity.
Figure 5
Figure 5
The summary of findings on chronic kidney diseases (CKD) risk among four phenotypes of Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No Obesity (MU-NO), Metabolically Healthy-obesity (MH-O), and Metabolically Unhealthy-obesity (MU-O). The full detail was reported in Tables 2, 3, Figs. 2, 3, and Supplementary Table S5. The adjusted HR were reported after adjusting for age, sex, education, smoking, and physical activity for CKD risk HR. The age-sex adjusted model was used for finding transition between states HR, and statistically significant HRs were presented.
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