Kidney Week

Abstract: SA-PO0389

Geographic Clustering of Cardiovascular-Kidney-Metabolic (CKM) Syndrome in the United States

Session Information

• CKM: Clinical - Epidemiology and Outcomes
  October 24, 2026 | Location: Exhibit Hall A, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Cardiovascular-Kidney-Metabolic Health

• 602 Cardiovascular-Kidney-Metabolic Health: Clinical

Authors

• Kapadia, Sohum, Loyola University Chicago Stritch School of Medicine, Chicago, Illinois, United States

Sohum Kapadia, MD

• Markossian, Talar, Loyola University Chicago Stritch School of Medicine, Chicago, Illinois, United States

Talar Markossian, PhD, MPH

• Franceschini, Nora, The University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, North Carolina, United States

Nora Franceschini, MD, MPH

• Rosas, Sylvia E., Joslin Diabetes Center, Boston, Massachusetts, United States

Sylvia E. Rosas, MD, MS, FASN

• Chang, Alexander R., Geisinger Medical Center, Danville, Pennsylvania, United States

Alexander R. Chang, MD, MS

• Kramer, Holly J., Loyola University Chicago Stritch School of Medicine, Chicago, Illinois, United States

Holly J. Kramer, MD, MPH

Background

Cardiovascular-kidney-metabolic (CKM) syndrome reflects the interconnected burden of cardiovascular disease (CVD), chronic kidney disease (CKD), obesity, and diabetes mellitus (DM). These conditions disproportionately affect socioeconomically disadvantaged populations, but less is known about county-level clustering of overlapping CKM burden, which may inform targeted public health interventions. We examined the geographic distribution of CKM burden and how socioeconomic adjustment influenced regional patterns.

Methods

County-level prevalence estimates for CVD, CKD, obesity, and DM were obtained from the 2023 Behavioral Risk Factor Surveillance System. County-level CKM burden was defined using the lowest standardized Z score (unadjusted and adjusted for county-level demographics, income, and poverty) among the four conditions, thereby identifying counties in which all CKM components were simultaneously elevated. CKM burden was categorized as high (Z > 1), average, or low (Z ≤ −1). Moran’s I evaluated spatial autocorrelation and clustering of CKM burden. A Bayesian spatial model quantified residual spatial structure.

Results

Significant spatial clustering of CKM burden was observed (Moran’s I = 0.55, p <0.001; Fig. 1, left panel), with high-burden clusters concentrated in the Southeast and Appalachia. After adjustment (Table 1), significant spatial clustering persisted (Moran’s I = 0.28, p <0.001; Fig. 1, right panel), particularly in the Southeastern U.S. In Bayesian models, approximately 58% of residual variation was spatially structured.

Conclusion

CKM burden demonstrates substantial geographic clustering across the U.S., particularly in the Southeast and Appalachia. Persistent clustering after demographic and socioeconomic adjustment suggests additional structural, environmental, or regional contributors to disproportionate CKM burden.

Table 1: CKM Prevalence Per County Both Unadjusted and with SES-Adjustment

Figure 1: Spatial Distribution of CKM Burden