Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020

John W Ostrominski; Suzanne V Arnold; Javed Butler; Gregg C Fonarow; Jamie S Hirsch; Swetha R Palli; Bonnie M K Donato; Christina M Parrinello; Thomas O’Connell; Eric B Collins; Jonathan J Woolley; Mikhail N Kosiborod; Muthiah Vaduganathan

doi:10.1001/jamacardio.2023.3241

. 2023 Sep 27;8(11):1050–1060. doi: 10.1001/jamacardio.2023.3241

Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020

John W Ostrominski ¹, Suzanne V Arnold ², Javed Butler ^3,⁴, Gregg C Fonarow ⁵, Jamie S Hirsch ⁶, Swetha R Palli ⁷, Bonnie M K Donato ⁷, Christina M Parrinello ⁸, Thomas O’Connell ⁹, Eric B Collins ¹⁰, Jonathan J Woolley ⁹, Mikhail N Kosiborod ², Muthiah Vaduganathan ^1,^✉

¹Brigham and Women’s Hospital Heart & Vascular Center and Harvard Medical School, Boston, Massachusetts

²Saint Luke’s Mid America Heart Institute and University of Missouri–Kansas City, Kansas City

³Baylor Scott and White Research Institute, Dallas, Texas

⁴Department of Medicine, University of Mississippi, Jackson

⁵Division of Cardiology, University of California, Los Angeles Medical Center, David Geffen School of Medicine, Los Angeles

⁶Division of Kidney Diseases and Hypertension, Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Great Neck, New York

⁷Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut

⁸Pine Mountain Consulting, LLC, Redding, Connecticut

⁹Medicus Economics, LLC, Boston, Massachusetts

¹⁰Medicus Economics, LLC, Philadelphia, Pennsylvania

Accepted for Publication: August 4, 2023.

Published Online: September 27, 2023. doi:10.1001/jamacardio.2023.3241

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Corresponding Author: Muthiah Vaduganathan, MD, MPH, Brigham and Women’s Hospital Heart & Vascular Center, Harvard Medical School, 75 Francis St, Boston, MA 02115 (mvaduganathan@bwh.harvard.edu).

Author Contributions: Drs Ostrominski and Vaduganathan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Ostrominski, Butler, Fonarow, Palli, Donato, O’Connell, Collins, Woolley, Vaduganathan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Ostrominski, Butler, Donato, O’Connell, Vaduganathan.

Statistical analysis: Parrinello, O’Connell, Collins, Woolley.

Obtained funding: Palli, Donato, Vaduganathan.

Administrative, technical, or material support: Donato, Parrinello, O’Connell, Woolley.

Supervision: Butler, Palli, Donato, O’Connell, Kosiborod, Vaduganathan.

Conflict of Interest Disclosures: Dr Butler reported receiving personal fees from Abbott, American Regent, Amgen, Applied Therapeutic, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardiac Dimension, Cardior, CVRx, Cytokinetics, Edwards, Element Science, Innolife, Impulse Dynamics, Imbria, Inventiva, Lexicon, Lilly, LivaNova, Janssen, Medtronics, Merck, Occlutech, Novartis, Novo Nordisk, Pfizer, Pharmacosmos, Pharmain, Roche, Sequana, SQ Innovation, and Vifor. Dr Fonarow reported receiving personal fees from Abbott, Amgen, AstraZeneca, Bayer, Cytokinetics, Eli Lilly, Janssen, Medtronic, Merck, Novartis, and Pfizer outside the submitted work; and serving as an associate section editor for JAMA Cardiology. Dr Hirsch reported receiving personal fees from Boehringer Ingelheim outside the submitted work. Ms Palli reported being employed by Boehringer Ingelheim, which has a product indicated for managing cardiac, renal, and metabolic conditions, during the conduct of the study. Dr Donato reported being employed by Boehringer Ingelheim, which has a product indicated for managing CRM conditions. Dr Parrinello reported receiving personal fees from Medicus Economics during the conduct of the study; personal fees from Medicus Economics, Collective Acumen, ConcertAI, EQRx, Evidation Health, Canopy Care, Flatiron Health, Greenleaf Health, Jazz Pharmaceuticals, Sensorum Health, TTi Health Research & Economics, and Clue by Biowink outside the submitted work. Messrs O’Connell, Collins, and Woolley are employees of Medicus Economics, a health economics and outcomes research consultancy that received consulting fees from Boehringer Ingelheim during the course of the study and have received consulting fees from other biopharmaceutical industry clients. Dr Kosiborod reported receiving personal fees from Alnylam, AstraZeneca, Amgen, Bayer, Boehringer Ingelheim, Cytokinetics, Dexcom, Eli Lilly, Janssen, Lexicon, Merck, Novo Nordisk, Pharmacosmos, Pfizer, scPharmaceuticals, Esperion Therapeutics, Structure Therapeutics, Vifor Pharma, Youngene Therapeutics, 35Pharma, and Applied Therapeutics to his institution; and grants from Boehringer Ingelheim and AstraZeneca to his institution outside the submitted work. Dr Vaduganathan reported receiving grants from Amgen, AstraZeneca, American Regent, Bayer, Boehringer Ingelheim, and Roche Diagnostics; receiving consulting fees from Amgen, AstraZeneca, American Regent, Baxter HealthCare, Bayer, Boehringer Ingelheim, Cytokinetics, Relypsa, Lexicon Pharmaceuticals, Tricog Health, Novo Nordisk, and Chiesi; serving on the advisory board for Amgen, AstraZeneca, American Regent, Baxter HealthCare, Bayer, Boehringer Ingelheim, Cytokinetics, Relypsa, Lexicon Pharmaceuticals, Tricog Health, Novo Nordisk, and Chiesi; receiving speaker fees AstraZeneca, Novartis, and Roche Diagnostics; and serving on a clinical trial committee for AstraZeneca, Novartis, Galmed, Occlutech, and Impulse Dynamics outside the submitted work. No other disclosures were reported.

Funding/Support: Analyses were performed by Medicus Economics, with support from Boehringer Ingelheim Pharmaceuticals.

Role of the Funder/Sponsor: Boehringer Ingelheim Pharmaceuticals had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

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Corresponding author.

PMCID: PMC10535010 PMID: 37755728

Key Points

Question

How often do cardiac, renal, and metabolic conditions coexist among US adults?

Findings

In this serial cross-sectional cohort study of 11 607 adults, more than 1 in 4 participants had at least 1 cardiac, renal, and metabolic condition. Cardiac, renal, and metabolic multimorbidity was observed in 8% of participants (nearly 1 in 4 ≥65 years) and increased significantly (from 5.3% to 8.0%) between 1999-2000 and 2017-2020; higher comorbidity burden was associated with older age, male sex, self-reported non-Hispanic Black race or ethnicity, and adverse socioeconomic characteristics.

Meaning

These findings suggest cardiac, renal, and metabolic multimorbidity is prevalent and increasing among US adults, highlighting the importance of collaborative and comprehensive management strategies.

Abstract

Importance

Individually, cardiac, renal, and metabolic (CRM) conditions are common and leading causes of death, disability, and health care–associated costs. However, the frequency with which CRM conditions coexist has not been comprehensively characterized to date.

Objective

To examine the prevalence and overlap of CRM conditions among US adults currently and over time.

Design, Setting, and Participants

To establish prevalence of CRM conditions, nationally representative, serial cross-sectional data included in the January 2015 through March 2020 National Health and Nutrition Examination Survey (NHANES) were evaluated in this cohort study. To assess temporal trends in CRM overlap, NHANES data between 1999-2002 and 2015-2020 were compared. Data on 11 607 nonpregnant US adults (≥20 years) were included. Data analysis occurred between November 10, 2020, and November 23, 2022.

Main Outcomes and Measures

Proportion of participants with CRM conditions, overall and stratified by age, defined as cardiovascular disease (CVD), chronic kidney disease (CKD), type 2 diabetes (T2D), or all 3.

Results

From 2015 through March 2020, of 11 607 US adults included in the analysis (mean [SE] age, 48.5 [0.4] years; 51.0% women), 26.3% had at least 1 CRM condition, 8.0% had at least 2 CRM conditions, and 1.5% had 3 CRM conditions. Overall, CKD plus T2D was the most common CRM dyad (3.2%), followed by CVD plus T2D (1.7%) and CVD plus CKD (1.6%). Participants with higher CRM comorbidity burden were more likely to be older and male. Among participants aged 65 years or older, 33.6% had 1 CRM condition, 17.1% had 2 CRM conditions, and 5.0% had 3 CRM conditions. Within this subset, CKD plus T2D (7.3%) was most common, followed by CVD plus CKD (6.0%) and CVD plus T2D (3.8%). The CRM comorbidity burden was disproportionately high among participants reporting non-Hispanic Black race or ethnicity, unemployment, low socioeconomic status, and no high school degree. Among participants with 3 CRM conditions, nearly one-third (30.5%) did not report statin use, and only 4.8% and 3.0% used glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors, respectively. Between 1999 and 2020, the proportion of US adults with multiple CRM conditions increased significantly (from 5.3% to 8.0%; P < .001 for trend), as did the proportion having all 3 CRM conditions (0.7% to 1.5%; P < .001 for trend).

Conclusions and Relevance

This cohort study found that CRM multimorbidity is increasingly common and undertreated among US adults, highlighting the importance of collaborative and comprehensive management strategies.

This cohort study investigates the prevalence and overlap of cardiac, renal, and metabolic conditions in US adults, using data from the National Health and Nutrition Examination Survey.

Introduction

Cardiac, renal, and metabolic (CRM) conditions are leading causes of morbidity and mortality in the US, with cardiovascular disease (CVD), chronic kidney disease (CKD), and type 2 diabetes (T2D) estimated to account for approximately 1 in every 3 deaths in the contemporary era.¹ Cardiovascular, kidney, and metabolic function is deeply interconnected,² with disease onset in each system often preceded by shared pathophysiology and risk factors, including dysglycemia, dyslipidemia, hypertension, and obesity.^2,3 As such, impairment in one system may promote and amplify dysfunction of the others, with implications for subsequent morbidity and mortality.^4,5,6,7 Owing to unique pathways of comorbidity attainment and recent therapeutic innovations targeting specific forms of CRM overlap, such as sodium-glucose cotransporter 2 inhibitors, individual CRM intersections are increasingly clinically and therapeutically relevant.⁸ Accordingly, contemporary clinical practice guidelines from both US-based^9,10 and international organizations^2,11 call for collaborative and comprehensive management of patients with CRM conditions, and multidisciplinary stakeholders have been convened explicitly to develop care-optimizing strategies for high-risk patients with CRM overlap.¹²

Among US adults, the estimated prevalence of individual CRM conditions is 9% to 11% for CVD,¹³ 15% for CKD,^13,14 and 13% for diabetes,¹⁵ with approximately 95% of diabetes attributed to T2D. However, the frequency with which CRM conditions coexist among patients in the US has not been comprehensively characterized, to our knowledge, and clarification of contemporary trends in the prevalence of CRM overlap may help inform clinicians, policy makers, research efforts, and health care service design. In this study, nationally representative data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2020 were used to examine the current and evolving prevalence of individual and overlapping CRM conditions among US adults.

Methods

Study Population

NHANES is a cross-sectional survey conducted by the National Center for Health Statistics and selects participants by using a complex, 4-stage probability sampling design. It collects information on the health and nutritional status of the civilian, noninstitutionalized (eg, not in prison or a nursing home) population in the US.¹⁶ Starting in 1999, data have been continuously collected and released in 2-year cycles. The continuous NHANES protocols for this cohort study were approved by the National Center for Health Statistics institutional review board, and written informed consent was obtained from all participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Overall, participants from the 1999-2000 to 2017 through March 2020 prepandemic continuous NHANES survey cycles were included in the analysis. Data from 2015 to 2020 were used to establish the contemporary prevalence of CRM conditions, whereas all survey cycles from 1999 to 2020 were used to evaluate trends in the prevalence of CRM conditions over time. The 2015-2016 and 2017 through March 2020 survey cycles were combined to improve reliability and precision of prevalence estimates of multiple CRM conditions and to enable examination of more detailed clinical classifications (ie, “segments”) within CRM conditions (eg, types of CVD and CKD stages). Participants were restricted to those aged 20 years or older who attended the clinical examination, were nonpregnant, and had sufficient information to determine their status in terms of CRM conditions and risk factors, as defined in eTable 1 in Supplement 1.

Definitions of CRM Conditions and CRM Risk Factors

Owing to the clinical and therapeutic relevance of specific forms of CRM overlap (eg, CKD plus T2D and CVD plus T2D),^2,9,10,11 we elected to analyze CRM overlap both by type and number (0, 1, 2, and 3) of CRM conditions. Cardiovascular disease was defined as atherosclerotic CVD (ASCVD), heart failure, or both. Specifically, types of CVD included in the analysis were self-report of a health care professional–derived diagnosis of heart failure, coronary heart disease, myocardial infarction, or stroke.¹⁷

Kidney impairment was defined according to estimated glomerular filtration rate and albuminuria.¹⁸ In NHANES, the urinary albumin to creatinine ratio and serum creatinine measures were available for participants who completed the medical examination (see the eAppendix in Supplement 1 for laboratory value collection details). To estimate glomerular filtration rate, we applied the 2021 race- and ethnicity-free Chronic Kidney Disease Epidemiology Collaboration creatinine equation.¹⁹ In accordance with estimated glomerular filtration rate and albumin to creatinine ratio values, participants were classified into 4 CKD risk levels: low, moderate, high, and very high.¹⁸ Moderate or higher risk levels of CKD were considered CKD in the current analysis.^2,11 Because confirmation of persistent kidney dysfunction during at least 3 months—required by current guidelines to distinguish acute vs chronic kidney impairment—is not possible using a cross-sectional data set such as NHANES, we compared our CKD estimates with those of the United States Renal Data System 2020 annual report, another epidemiologic analysis of CKD in the US.¹⁴

Metabolic disease was defined as the presence of T2D, which was defined by either self-report (diagnosed diabetes) or glycated hemoglobin level at least 6.5% in the absence of a self-reported diagnosis (undiagnosed diabetes).⁹ Among participants with diagnosed diabetes, a treatment-based algorithm²⁰ was implemented to distinguish T2D from type 1 diabetes, using self-reported information on type and duration of medication use, premised on the basis that patients with type 1 diabetes would initiate insulin and not oral hypoglycemic agents within a year of diagnosis of diabetes. Participants considered to have T2D in the analysis were restricted to those with probable T2D and possible T2D, as well as those with undiagnosed diabetes, under the rationale that participants with a glycated hemoglobin level of at least 6.5% but not receiving a diagnosis before aged 20 years are most likely to have T2D vs type 1 diabetes.

In addition to CRM conditions, the analysis also examined the prevalence of key CRM risk factors³ and components of metabolic syndrome,²¹ including hypertension, prediabetes, obesity, and hypercholesterolemia. Definitions of CRM conditions and risk factors are detailed in eTable 1 in Supplement 1.

Evaluation of Temporal Trends in CRM Prevalence and Overlap

Trends over time in the prevalence and overlap of CRM conditions were also explored through comparison across survey cycles spanning 1999 through March 2020. To maximize reliability and precision, NHANES analytic guidelines suggest combining survey cycles when making intertemporal comparisons.²² Therefore, CRM prevalence was estimated for 1999-2002, 2003-2008, 2009-2014, and 2015 through March 2020. When estimated glomerular filtration rate was calculated, adjustments to creatinine were implemented to account for changes in instruments and measurement methods over time.

Statistical Analysis

Sampling weights provided by the National Center for Health Statistics were used to estimate prevalence representative of the civilian, noninstitutionalized US population aged 20 years or older.²² In all analyses, we used recommended sampling weights and the Taylor series linearization variance approximation method to account for the complex survey design.¹⁶ Reweighting for nonresponse was evaluated in the main analysis using the adjustment cell method,²³ which did not substantially affect point estimates. Hence, reweighting was not implemented in the complete case analysis presented herein, similar to other analyses.^24,25 Corresponding 95% CIs were calculated for all prevalence estimates, and results are reported indicating where relative SEs (the ratio of SE to prevalence estimate) exceed 30%, suggesting that prevalence estimates may be unreliable.²²

Population counts were calculated by multiplying weighted prevalence estimates by the 2020 American Community Survey 5-year population estimates,²⁶ which report a total of 244 657 212 persons aged 20 years or older. To calculate counts for the overall US noninstitutionalized adult population, prevalence estimates for persons younger than 65 years and those aged 65 years or older were multiplied by the respective population counts in each age range (192 294 395 and 52 362 817, respectively), aligning with a similar age distribution as implied by the NHANES weights, and were then summed.

Participant characteristics were estimated by groups based on number of CRM conditions. The prevalence of each CVD, CKD, and T2D condition, as well as prevalences for their intersecting segments, were estimated separately, yielding a set of 8 CRM “statuses”: none, CVD only, CKD only, T2D only, CVD plus CKD, CVD plus T2D, CKD plus T2D, or CVD plus CKD plus T2D. In participants with CVD, the distribution across segments of CVD (ASCVD only, heart failure only, or ASCVD and heart failure) was calculated by CRM status (CVD only, CVD plus CKD, CVD plus T2D, and CVD plus CKD plus T2D). In participants with CKD, the distribution across CKD risk levels (moderate, high, or very high CKD risk) was calculated by CRM status. In participants with T2D, the percentage of those with diagnosed T2D or undiagnosed diabetes was calculated by CRM status. Analyses were conducted in the overall population and stratified by age (<65 years vs ≥65 years). Prevalence of each CRM risk factor was estimated across the 8 CRM statuses.

For the temporal trends analysis, tests for linear trend were conducted with logistic regression. The independent variable was defined as the midpoint of each survey cycle (and was modeled continuously) and the dependent variable was defined as the CRM condition of interest (eg, presence of CVD only, presence of CVD plus CKD, presence of ≥1 CRM condition). The 95% CIs were calculated, and P values for the coefficient were calculated with a Wald test. All statistical analyses were conducted with R, version 4.0.2 (R Foundation for Statistical Computing), and 2-sided P < .05 was considered statistically significant. Data visualization was conducted in R and Excel, version 2140 (Microsoft). Data analysis occurred between November 10, 2020, and November 23, 2022.

Results

Study Population

Of the 11 607 US adults included in the analysis, mean (SE) age was 48.5 (0.4) years, 49.0% were men, and 51.0% were women. A total of 15.3% were Hispanic, 5.4% were Non-Hispanic Asian, 10.6% were Non-Hispanic Black, and 64.8% were Non-Hispanic White; 3.9% reported being of other race or ethnicity or multiracial (self-reported Mexican American or other Hispanic ethnicity). Selection of the study sample is illustrated in eFigure 1 in Supplement 1. The interview samples of the 2015-2016 and 2017 through March 2020 continuous NHANES survey cycles included 9971 and 15 560 participants, respectively. In the combined sample of 25 531 participants, 14 018 were aged 20 years or older and completed the medical examination. After exclusion of 2411 participants with insufficient data to determine CRM condition or risk factor status, 11 607 participants (unweighted sample) were included in the complete case analysis. Among these participants, 1820 had a self-reported diagnosis of diabetes (1730 T2D [1605 probable and 125 possible] and 90 non-T2D [57 type 1 diabetes and 33 inconclusive]). In addition, comparison of the CKD estimates in this analysis with the 2020 United States Renal Data System revealed good alignment (eTable 2 in Supplement 1).

Prevalence and Overlap of CRM Conditions

From 2015 through March 2020, 18.2% of US adults (population estimate, 44.9 million) had 1 CRM condition, 6.5% (16.3 million) had 2 CRM conditions, and 1.5% (3.8 million) had 3 CRM conditions; CKD was the most prevalent CRM condition (13.9%), followed by T2D (13.3%) and CVD (8.6%) (Table 1, Table 2, and Figure 1). The CRM comorbidity burden was disproportionately high among participants who were unemployed, of low socioeconomic status, and without a high school degree (Table 1). The most common CRM dyad was CKD plus T2D (3.2%), followed by CVD plus T2D (1.7%) and CVD plus CKD (1.6%) (Table 2 and Figure 1). Having multiple CVD conditions (ASCVD plus heart failure) was more common among participants with CVD plus CKD plus T2D status (23%) vs those with CVD alone (12%). Higher-risk stages of CKD were also more common in participants with overlapping CRM conditions compared with CKD alone (Figure 2).

Table 1. Distribution of Demographic, Clinical, and Socioeconomic Characteristics Overall and by Number of CRM Conditions in US Adults, 2015 Through March 2020.

Characteristic	Weighted % (95% CI)
	Overall	No. of CRM conditions
	Overall	0	1	2	3
Unweighted No.	11 607	7724	2585	1031	267
Overall	100	73.7 (72.4-75.0)	18.2 (17.1-19.3)	6.5 (5.9-7.2)	1.5 (1.2-1.8)
Demographic characteristics
Age, y
20-44	43.0 (40.8-45.3)	52.5 (50.0-54.9)	20.6 (18.7-22.4)	8.7 (5.8-11.5)	3.3 (0.7-5.8)^a
45-64	36.3 (34.8-37.8)	35.1 (33.3-37.0)	41.3 (38.6-43.9)	37.2 (32.0-42.3)	27.8 (19.3-36.2)
≥65	20.7 (18.9-22.4)	12.4 (10.8-14.0)	38.2 (35.3-41.1)	54.2 (48.3-60.0)	69.0 (60.7-77.3)
Sex
Male	49.1 (47.9-50.4)	49.6 (48.2-51.0)	44.3 (41.4-47.3)	55.2 (50.5-60.0)	58.9 (49.7-68.0)
Female	50.9 (49.6-52.1)	50.4 (49.0-51.8)	55.7 (52.7-58.6)	44.8 (40.0-49.5)	41.1 (32.0-50.3)
Race and ethnicity
Hispanic^b	15.3 (12.5-18.0)	15.8 (13.0-18.6)	14.1 (11.1-17.1)	13.7 (9.8-17.7)	9.7 (6.2-13.1)
Non-Hispanic Asian	5.4 (4.1-6.8)	5.6 (4.2-6.9)	5.4 (3.8-7.0)	4.6 (3.0-6.3)	2.9 (1.2-4.6)
Non-Hispanic Black	10.6 (8.3-12.9)	9.6 (7.5-11.7)	13.0 (9.8-16.2)	14.0 (10.3-17.7)	15.3 (10.4-20.1)
Non-Hispanic White	64.8 (60.6-69.0)	65.4 (61.5-69.4)	63.1 (57.3-68.9)	61.5 (54.9-68.0)	68.7 (61.0-76.3)
Other or multiracial	3.9 (3.4-4.5)	3.6 (3.1-4.1)	4.5 (3.0-5.9)	6.2 (4.2-8.1)	3.5 (1.2-5.9)^a
CRM conditions
Cardiovascular disease (any ASCVD or HF)	8.6 (7.6-9.7)	0	21.1 (18.3-23.9)	50.4 (44.0-56.8)	100.0 (100.0-100.0)
ASCVD and HF	1.6 (1.2-2.0)	0	2.5 (1.6-3.3)	12.2 (8.8-15.5)	23.4 (14.9-32.0)
Only ASCVD	6.2 (5.5-7.0)	0	17.2 (14.6-19.9)	33.2 (28.8-37.6)	62.1 (53.0-71.2)
Only HF	0.8 (0.6-1.0)	0	1.4 (0.8-1.9)	5.0 (3.4-6.7)	14.5 (8.8-20.1)
CKD (any non–low CKD risk)	13.9 (12.9-14.9)	0	41.4 (38.8-43.9)	74.0 (68.7-79.2)	100.0 (100.0-100.0)
Moderate	10.2 (9.4-11.1)	0	34.0 (31.4-36.6)	49.1 (43.5-54.7)	56.3 (47.5-65.1)
High	2.5 (2.1-2.8)	0	5.6 (4.3-6.8)	16.0 (13.3-18.7)	29.0 (19.2-38.7)
Very high	1.1 (1.0-1.3)	0	1.8 (1.3-2.4)	8.9 (7.5-10.3)	14.7 (10.2-19.3)
Type 2 diabetes	13.3 (12.4-14.1)	0	37.6 (34.4-40.7)	75.6 (72.6-78.6)	100.0 (100.0-100.0)
Diagnosed diabetes	11.0 (10.3-11.7)	0	29.5 (26.5-32.5)	65.6 (62.3-68.8)	90.8 (85.2-96.4)
Undiagnosed diabetes	2.3 (2.0-2.6)	0	8.1 (6.8-9.4)	10.0 (8.0-12.1)	9.2 (3.6-14.8)^a
CRM risk factors
Hypertension	38.5 (36.9-40.1)	33.6 (31.7-35.6)	48.5 (46.2-50.9)	59.0 (54.5-63.6)	65.8 (57.1-74.5)
Prediabetes	25.9 (24.9-27.0)	27.3 (26.1-28.4)	27.8 (24.9-30.8)	11.5 (9.2-13.9)	0.0 (0.0-0.0)
Obesity	41.2 (39.3-43.1)	37.1 (35.0-39.1)	49.4 (46.4-52.3)	59.9 (54.9-64.8)	64.1 (58.2-70.0)
Hypercholesterolemia	29.0 (28.0-30.0)	20.6 (19.3-21.9)	47.0 (45.0-49.1)	64.3 (59.8-68.8)	69.6 (60.8-78.3)
SES characteristics^c
Unemployed^d	16.7 (15.5-17.9)	15.2 (14.0-16.4)	20.8 (18.6-23.1)	20.2 (17.1-23.4)	28.1 (22.3-33.9)
Without HS degree^e	11.6 (10.2-13.1)	9.9 (8.5-11.3)	15.5 (13.0-18.0)	18.6 (15.1-22.0)	20.3 (15.9-24.6)
Below poverty level^f	12.8 (11.2-14.3)	12.1 (10.4-13.7)	14.1 (12.0-16.1)	16.4 (13.0-19.7)	17.0 (9.1-24.9)

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Abbreviations: ASCVD, atherosclerotic cardiovascular disease; CKD, chronic kidney disease; CRM, cardiac, renal, and metabolic; HF, heart failure; HS, high school; SES, socioeconomic status.

^{^a}

Point estimates may be unreliable (relative SE ≥30%).

^{^b}

Reflects the combination of self-reported Mexican American and other Hispanic race and ethnicity.

^{^c}

Prevalence of unemployment and no HS degree was calculated among the 11 588 participants who were not missing those variables. Prevalence of income below the poverty level was calculated among the 10 260 participants who additionally were not missing income level.

^{^d}

Unemployment status was defined as self-report of not having worked at a job or business in the past week. Participants who self-reported that they were a student or retired were not considered to be unemployed.

^{^e}

Not having a high school degree was defined as self-report of less than a 12th-grade education.

^{^f}

Income below the poverty level was based on reported family income less than poverty guidelines used to determine financial eligibility for federal programs.

Table 2. Prevalence of CRM Conditions in US Adults, Overall and Stratified by Age, 2015 Through March 2020.

CRM conditions	Overall		Aged <65 y		Aged ≥65 y
CRM conditions	Prevalence, weighted % (95% CI)	Population (millions)^a	Prevalence, weighted % (95% CI)	Population (millions)^a	Prevalence, weighted % (95% CI)	Population (millions)^a
Unweighted No.	11 607		8763		2844
None	73.7 (72.4-75.1)	179.7	81.4 (80.4-82.5)	156.5	44.3 (41.1-47.5)	23.2
CVD only	3.8 (3.2-4.4)	9.5	2.5 (1.9-3.1)	4.8	9.0 (7.7-10.3)	4.7
CKD only	7.5 (6.9-8.2)	18.5	5.6 (5.0-6.3)	10.8	14.8 (13.0-16.6)	7.7
T2D only	6.9 (6.2-7.5)	16.9	6.1 (5.4-6.8)	11.7	9.8 (8.4-11.2)	5.1
CVD + CKD	1.6 (1.3-1.9)	4.1	0.5 (0.4-0.6)	1.0	6.0 (4.9-7.0)	3.1
CVD + T2D	1.7 (1.3-2.1)	4.1	1.1 (0.7-1.6)	2.1	3.8 (3.0-4.7)	2.0
CKD + T2D	3.2 (2.8-3.7)	8.1	2.2 (1.8-2.6)	4.2	7.3 (6.0-8.7)	3.8
CVD + CKD + T2D	1.5 (1.2-1.8)	3.8	0.6 (0.4-0.8)	1.2	5.0 (3.9-6.1)	2.6
Any CVD	8.6 (7.6-9.7)	21.5	4.7 (3.9-5.5)	9.0	23.8 (21.3-26.2)	12.5
Any CKD	13.9 (12.9-14.9)	34.4	8.9 (8.1-9.6)	17.1	33.1 (30.6-35.6)	17.3
Any T2D	13.3 (12.4-14.1)	32.8	10.0 (9.3-10.7)	19.2	26.0 (23.5-28.4)	13.6
Any CVD, CKD, or T2D	26.3 (25.0-27.6)	64.9	18.6 (17.5-19.6)	35.8	55.7 (52.5-58.9)	29.2
With multiple^b	30.6 (28.2-32.9)	20.0	23.5 (20.1-26.9)	8.4	39.6 (35.7-43.6)	11.5

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Abbreviations: CKD, chronic kidney disease; CRM, cardiac, renal, and metabolic; CVD, cardiovascular disease; NHANES, National Health and Nutrition Examination Survey; T2D, type 2 diabetes.

^{^a}

Prevalence estimates for persons younger than 65 years and those aged 65 years or older were multiplied by the respective population counts in each age range (192 294 395 and 52 362 817, respectively), aligning with the distribution of ages younger than 65 years and 65 years or older implied by the NHANES weights, and were then summed to derive the overall population estimates. Unweighted Ns: 8763 individuals younger than 65 years and 2844 aged 65 years or older.

^{^b}

Reflects CRM prevalence attributable to CVD plus CKD, CVD plus T2D, CKD plus T2D, and CVD plus CKD plus T2D (ie, excluding CVD only, CKD only, and T2D only) among participants with at least 1 CRM condition.

Figure 1. — Percentage and weighted prevalence (in millions of persons) of noninstitutionalized US adults with CVD, CKD, and T2D overall (A), as well as single and overlapping CRM conditions (B). Error bars indicate 95% CIs. Unweighted Ns by CRM status: 486 participants with CVD only, 1000 with CKD only, 1099 with T2D only, 272 with CVD plus CKD, 213 with CVD plus T2D, 546 with CKD plus T2D, and 267 with CVD plus CKD plus T2D. CKD indicates chronic kidney disease; CVD, cardiovascular disease; and T2D, type 2 diabetes.

Figure 2. — Proportion of individuals with atherosclerotic CVD (ASCVD) and heart failure (HF) among those with CVD (A), the distribution of selected Kidney Disease Improving Global Outcomes risk stages among individuals with CKD (B), and the proportion of previously diagnosed vs undiagnosed diabetes among all individuals with T2D (C), all by CRM status. Unweighted Ns by CRM status: 486 individuals with CVD only, 1000 with CKD only, 1099 with T2D only, 272 with CVD plus CKD, 213 with CVD plus T2D, 546 with CKD plus T2D, and 267 with CVD plus CKD plus T2D.

^aPoint estimates may be unreliable (relative SE ≥30%).

Temporal Trends in Prevalence of CRM Conditions and Overlap

From 1999 through March 2020, the proportion of US adults with at least 1 CRM condition increased from 21.2% in 1999-2002 to 26.3% in 2015-2020 (P < .001 for test of linear trend), accompanied by a corresponding reduction in the proportion without a CRM condition (from 78.8% to 73.7%; P < .001 for trend) (Table 3). Substantial growth in the prevalence of T2D (from 7.5% to 13.3%; P < .001 for trend) and total CVD (from 6.8% to 8.6%; P = .02 for trend) was observed, associated with statistically significant increases in CVD plus T2D (from 0.7% to 1.7%; P < .001 for trend) and CKD plus T2D (from 2.2% to 3.2%; P < .001 for trend) overlap. Overall, the proportion of US adults with at least 2 CRM conditions increased significantly between 1999-2000 and 2017-2020 (from 5.3% to 8.0%; P < .001 for trend), and the proportion at the triple intersection (CVD plus CKD plus T2D) more than doubled (from 0.7% to 1.5%; P < .001 for trend).

Table 3. Prevalence of CRM Conditions in US Adults, 1999-2020.

CRM conditions	Weighted prevalence, % (95% CI)				P value^a
CRM conditions	1999-2002 (n = 7576)	2003-2008 (n = 12 534)	2009-2014 (n = 14 558)	2015-2020 (n = 11 607)	P value^a
None	78.8 (77.4-80.2)	77.2 (75.7-78.6)	76.4 (75.4-77.5)	73.7 (72.4-75.1)	<.001
CVD only	3.7 (3.1-4.2)	3.8 (3.3-4.3)	3.3 (3.0-3.7)	3.8 (3.2-4.4)	.96
CKD only	8.3 (7.4-9.2)	8.0 (7.3-8.7)	7.7 (7.1-8.4)	7.5 (6.9-8.2)	.16
T2D only	3.9 (3.4-4.4)	4.9 (4.4-5.3)	5.9 (5.4-6.3)	6.9 (6.2-7.5)	<.001
CVD + CKD	1.8 (1.5-2.0)	1.7 (1.5-2.0)	1.7 (1.4-1.9)	1.6 (1.3-1.9)	.34
CVD + T2D	0.7 (0.5-0.9)	1.0 (0.8-1.3)	1.2 (1.0-1.3)	1.7 (1.3-2.1)	<.001
CKD + T2D	2.2 (1.8-2.6)	2.2 (1.9-2.6)	2.6 (2.3-2.9)	3.2 (2.8-3.7)	<.001
CVD + CKD + T2D	0.7 (0.5-0.9)	1.2 (1.0-1.5)	1.3 (1.1-1.5)	1.5 (1.2-1.8)	<.001
Any CVD	6.8 (6.0-7.7)	7.8 (7.0-8.6)	7.4 (6.8-8.0)	8.6 (7.6-9.7)	.02
Any CKD	12.9 (12.0-13.9)	13.2 (12.3-14.1)	13.2 (12.4-14.1)	13.9 (12.9-14.9)	.19
Any T2D	7.5 (6.6-8.3)	9.4 (8.6-10.1)	10.9 (10.2-11.5)	13.3 (12.4-14.1)	<.001
Any CRM condition	21.2 (19.8-22.6)	22.8 (21.4-24.3)	23.6 (22.5-24.6)	26.3 (24.9-27.6)	<.001
≥2 Conditions	5.3 (4.8-5.9)	6.2 (5.6-6.8)	6.7 (6.1-7.2)	8.0 (7.3-8.8)	<.001

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Abbreviations: CKD, chronic kidney disease; CRM, cardiac, renal, and metabolic; CVD, cardiovascular disease; T2D, type 2 diabetes.

^{^a}

Calculated from a Wald test of the coefficient for the midpoint of each survey cycle (as a continuous variable) from a weighted logistic regression model.

Age-Stratified Analyses

Increasing CRM burden was associated with increasing age such that participants aged 65 years or older composed 69.0% of those with 3 CRM conditions compared with 12.4% of those without a CRM condition (Table 1). Among participants aged 65 years or older, 33.6% had 1 CRM condition, 17.1% had 2 CRM conditions, and 5.0% had 3 CRM conditions; CKD was the most prevalent CRM condition (33.1%), followed by T2D (26.0%) and CVD (23.8%). The most common CRM dyad among participants aged 65 years or older was CKD plus T2D (7.3%), followed by CVD plus CKD (6.0%) and CVD plus T2D (3.8%) (Table 2; eFigure 2 in Supplement 1).

A higher proportion of higher-risk CKD was observed across CRM statuses in older participants, whereas a higher proportion of T2D was undiagnosed in younger participants vs older ones (eFigure 3 in Supplement 1). The relative proportions of ASCVD and heart failure were similar among individuals with CVD alone and CVD plus T2D between age groups. Younger individuals with CVD plus CKD plus T2D appeared to have a greater proportion of isolated heart failure, whereas concomitant ASCVD and heart failure was more common among older individuals.

Sex-Stratified and Race- and Ethnicity-Stratified Analyses

Women experienced a slightly higher burden of any CVD, CKD, or T2D compared with men (26.9% vs 25.6%), although men were more likely to have multiple CRM conditions (9.1% vs 7.0%) (eFigure 2 and eTable 3 in Supplement 1). The proportion of non-Hispanic Black participants generally increased with increasing CRM comorbidity burden (Table 1), associated largely with a higher burden of T2D and associated overlap (eFigure 2 and eTable 3 in Supplement 1). The proportion of participants self-identifying as Hispanic decreased with increasing CRM comorbidity burden (Table 1). Although Hispanic participants demonstrated the second-highest prevalence of overall T2D, they had the lowest prevalence of any CVD, any CKD, and multiple CRM conditions.

Prescription Medication Use by CRM Status

Statin use was reported by 47.0% of participants with T2D alone, 44.6% with CKD plus T2D, 75.6% with CVD plus T2D, and 69.5% with CVD plus CKD plus T2D (eTable 4 in Supplement 1). Among participants with T2D alone, metformin (53.4%) and sulfonylureas (17.1%) were the most common antihyperglycemic agents, followed by insulin (12.8%), glucagon-like peptide 1 receptor agonists (6.0%) dipeptidyl peptidase-4 inhibitors (5.8%), sodium-glucose cotransporter 2 inhibitors (3.4%), and thiazolidinediones (2.3%). Among individuals with CVD plus T2D, use of sulfonylureas (22.6%), insulin (17.5%), dipeptidyl peptidase-4 inhibitors (8.4%), and sodium-glucose cotransporter 2 inhibitors (7.4%) was higher compared with that among individuals with T2D alone, whereas glucagon-like peptide 1 receptor agonist use was lower (2.2%). Among individuals with CKD plus T2D, use of sulfonylureas (27.4%), and insulin (20.7%), and dipeptidyl peptidase-4 inhibitors (8.1%) was higher compared with that among individuals with T2D alone, with lower rates of sodium-glucose cotransporter 2 inhibitor (2.5%) and glucagon-like peptide 1 receptor agonist (2.2%) use. Metformin use was similar for T2D alone (53.4%), CVD plus T2D (52.8%), and CKD plus T2D (52.2%), all higher than for CVD plus CKD plus T2D (36.3%). Conversely, insulin use was higher for individuals with CVD plus CKD plus T2D (34.1%) compared with T2D alone (12.8%), CVD plus T2D (17.5%), and CKD plus T2D (20.7%). Use of glucagon-like peptide 1 receptor agonist and sodium-glucose cotransporter 2 inhibitors was rare, even among patients with CVD plus CKD plus T2D (4.8% and 3.0%, respectively). Less than half of participants with CVD plus CKD (46.8%) reported use of either an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker, with higher use among those with CKD plus T2D (60.7%), CVD plus T2D (66.2%), and CVD plus CKD plus T2D (68.8%).

Prevalence of Key CRM Risk Factors

Prediabetes, hypercholesterolemia, hypertension, and obesity were present in 25.9%, 29.0%, 38.5%, and 41.2% of all participants (Table 1), respectively. Among individuals without established CRM conditions, hypercholesterolemia (20.6%), prediabetes (27.3%), hypertension (33.6%), and obesity (37.1%) were common. The burden of each CRM risk factor was associated with increasing CRM comorbidity burden (eFigure 4 in Supplement 1). Hypertension was particularly concentrated in participants with a CRM status including CKD, obesity in those with T2D, and prediabetes and hypercholesterolemia in participants with CVD.

Discussion

These findings demonstrate that CRM conditions and their overlap are common in a contemporary population-based sample of US adults. More than 1 in 4 US adults had a CRM condition, with overlapping CRM conditions present in nearly 1 in 10 participants. Among individuals aged 65 years or older, more than 1 in 2 had any CRM condition, and nearly 1 in 4 exhibited overlapping CRM conditions. We additionally showed the proportion of US adults with multiple CRM conditions to be increasing significantly over time (from 5.3% to 8.0% between 1999 and 2020). These data reinforce the need to embrace the complexities imposed by concurrent CRM conditions in research efforts, clinical practice, guideline development, and the formulation of public health policy.^5,27

Prior investigations have examined secular trends in individual CRM conditions, demonstrating a persistently high burden of ASCVD^28,29 and increasing rates of heart failure,^30,31 CKD,³² T2D,¹⁵ and obesity.^{33,34,35,36,37} Coupled with an aging population³⁸ and an increasing burden of cardiometabolic risk factors among younger adults,³⁴ these data suggested that CRM multimorbidity will become increasingly prevalent. This analysis, confirming the high and increasing prevalence of CRM multimorbidity in the United States within only the last 2 decades, further clarifies the scale of a potentially burgeoning CRM health crisis. Owing to the substantial and additive associations between concurrent CRM conditions and death, disability, and health care–associated costs,^5,27,39,40 these data highlight the critical need for further efforts to identify optimal care delivery pathways to minimize the repercussions of these population trends.

Major treatment gaps were observed across the CRM spectrum, which were generally commensurate with previous analyses.^41,42,43 In this analysis, treatment with statins and renin-angiotensin system inhibitors was suboptimal, even among individuals with the highest risk. For example, despite strong guideline recommendations, less than half of patients with concomitant T2D and CKD were treated with a statin. Furthermore, use of either sodium-glucose cotransporter 2 inhibitors or glucagon-like peptide 1 receptor agonist was rare, even among patients with all 3 CRM conditions. Owing to the proximity of this study timeline to the regulatory approval and guideline recommendations supporting use of these therapies, these observations may not reflect current clinical practice. However, these findings are consistent with other and more recent observational studies,^44,45,46,47 supporting the development of robust implementation strategies and even focused training pathways⁴⁸ to improve delivery of optimal care for the large and growing population with multiple CRM conditions. Consideration of the perspective of low- and middle-income countries, where the burden of CRM conditions and risk factors has accelerated, and where access and affordability of established primary and secondary preventive modalities remains severely limited,^49,50 prompts additional concern.

Although the increasing prevalence of key CRM risk factors with increasing CRM comorbidity burden observed in this analysis is unsurprising, the high prevalence of hypertension, obesity, and prediabetes among patients without CRM conditions emphasizes important targets for prevention of downstream CRM conditions and multimorbidity. In particular, obesity was highly prevalent in this analysis, observed in more than 1 in 3 individuals without any CRM disease. In addition to its robust contribution to cardiac, renal, and metabolic and all-cause risk,^{51,52,53,54,55,56} excess adiposity is associated with multimorbidity.⁵⁷ However, despite widespread recognition of the global obesity pandemic^58,59 and significant advances in antiobesity pharmacotherapy,⁶⁰ use among eligible patients remains exceedingly low.^61,62,63 Although cardiovascular outcome trials have not yet formally reported results, improved population-level efforts capable of targeting excess adiposity along with established CRM risk factors offer great potential in averting and ameliorating CRM multimorbidity.⁶⁴

Despite that results of prior studies showed lower rates of T2D diagnosis, CRM comorbidity burden was disproportionately high among non-Hispanic Black participants. These observations extend the results of prior analyses showing lower linkage to care,⁴¹ comorbidity control,^41,65 and access to foundational CRM pharmacotherapies^44,66,67 despite higher rates of cardiovascular risk factors,³⁶ ultimately associated with persistent disparities in cardiometabolic mortality among individuals with self-reported Black race or ethnicity.^68,69 Participants with self-reported Hispanic race or ethnicity were also observed to have a high prevalence of T2D, consistent with other analyses.⁶⁵ Although Hispanic participants exhibited a lower prevalence of CRM overlap compared with non-Hispanic Black participants, increasing rates of obesity,³⁷ CKD,⁷⁰ and age-adjusted mortality due to stroke and heart failure⁷¹ similarly provide justification for concern when the low rates of access to key therapies^44,66 are considered in this group. Lower socioeconomic status and unemployment were also associated with greater CRM comorbidity burden. Socioeconomic inequalities are well-recognized and powerful determinants of CRM risk,⁷² and low household income is associated with undertreatment even among commercially insured patients.⁶⁶ This analysis reinforces the critical need for strategically designed implementation interventions that systematically address social determinants of health and health equity.

Taken together, this analysis shows CRM multimorbidity is increasingly prevalent and historically undertreated among US adults, supporting the development of team-based, comprehensive, and equitable management strategies to enable attainment of prevention and treatment goals throughout the life span and across the CRM continuum.

Limitations

This study has some limitations. First, NHANES consists of serial cross-sectional survey data from noninstitutionalized civilian adults in the US; hence, generalizability may be limited in other populations and settings. Second, because the algorithm used herein to distinguish diabetes types was informed by use of prescription medications and time from diagnosis of diabetes, it could be applied only to distinguish T2D vs type 1 diabetes for participants with diagnosed diabetes. Third, misclassification of T2D was possible owing to reliance on self-reported diagnosis and single-occasion measurement of glycated hemoglobin. As such, some patients determined to have undiagnosed T2D in this analysis might have been reclassified with serial glycated hemoglobin assessment. Furthermore, exclusion of oral glucose tolerance testing data—not available for all age groups or in all NHANES data years—may have alternatively resulted in underestimation of undiagnosed T2D.⁷³ Fourth, a small rightward shift in the distribution of glycated hemoglobin data from 2007 to 2010 was identified by the National Center for Health Statistics, of unknown cause after extensive investigation. However, other NHANES periods included in this analysis should not be affected.

Conclusions

This cohort study found that CRM multimorbidity is increasingly prevalent among US adults, with an especially high burden observed among individuals reporting older age, non-Hispanic Black race or ethnicity, and adverse socioeconomic characteristics. Additionally, key CRM risk factors were prevalent among participants with and without established CRM conditions, and major treatment gaps were observed across the CRM spectrum. These findings highlight the importance of collaborative and comprehensive management strategies for patients with or at risk for CRM conditions.

Supplement 1.

eAppendix. Supplemental Methods

eFigure 1. Flow Diagram of Inclusion/Exclusion of Study Population

eFigure 2. Prevalence and Overlap of CRM Conditions in US Adults, Stratified by Age, Sex, and Race/Ethnicity, 2015-March 2020

eFigure 3. Distribution Over Segments of C, R, and M Status, Stratified by Age, 2015-2020

eFigure 4. Prevalence of Key CRM Risk Factors by CRM Status in US Adults, 2015-March 2020

eTable 1. Study Definitions of CRM Conditions and Risk Factors

eTable 2. Comparison of Prevalence of CKD in the Study Population vs United States Renal Data System

eTable 3. Prevalence of CRM Conditions in US Adults, Stratified by Age, Race/Ethnicity, and Sex, 2015-2020

eTable 4. Prescription Medication Use in US Adults, by CRM Status, 2015-2020

eReferences.

Click here for additional data file.^{(614.7KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(16KB, pdf)}

References

1.Ahmad FB, Anderson RN. The leading causes of death in the US for 2020. JAMA. 2021;325(18):1829-1830. doi: 10.1001/jama.2021.5469 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Sarafidis P, Ferro CJ, Morales E, et al. SGLT-2 inhibitors and GLP-1 receptor agonists for nephroprotection and cardioprotection in patients with diabetes mellitus and chronic kidney disease: a consensus statement by the EURECA-m and the DIABESITY working groups of the ERA-EDTA. Nephrol Dial Transplant. 2019;34(2):208-230. doi: 10.1093/ndt/gfy407 [DOI] [PubMed] [Google Scholar]
3.Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration . Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol. 2014;2(8):634-647. doi: 10.1016/S2213-8587(14)70102-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Cherney DZI, Repetto E, Wheeler DC, et al. Impact of cardio-renal-metabolic comorbidities on cardiovascular outcomes and mortality in type 2 diabetes mellitus. Am J Nephrol. 2020;51(1):74-82. doi: 10.1159/000504558 [DOI] [PubMed] [Google Scholar]
5.Forman DE, Maurer MS, Boyd C, et al. Multimorbidity in older adults with cardiovascular disease. J Am Coll Cardiol. 2018;71(19):2149-2161. doi: 10.1016/j.jacc.2018.03.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.García-Donaire JA, Ruilope LM. Cardiovascular and renal links along the cardiorenal continuum. Int J Nephrol. 2011;2011:975782. doi: 10.4061/2011/975782 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Thomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol. 2011;6(10):2364-2373. doi: 10.2215/CJN.02180311 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Joseph JJ, Deedwania P, Acharya T, et al. ; American Heart Association Diabetes Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Clinical Cardiology; Council on Hypertension . Comprehensive management of cardiovascular risk factors for adults with type 2 diabetes: a scientific statement from the American Heart Association. Circulation. 2022;145(9):e722-e759. doi: 10.1161/CIR.0000000000001040 [DOI] [PubMed] [Google Scholar]
9.American Diabetes Association . Standards of Medical Care in Diabetes–2020 abridged for primary care providers. Clin Diabetes. 2020;38(1):10-38. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Rangaswami J, Bhalla V, de Boer IH, et al. ; American Heart Association Council on the Kidney in Cardiovascular Disease; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Lifestyle and Cardiometabolic Health . Cardiorenal protection with the newer antidiabetic agents in patients with diabetes and chronic kidney disease: a scientific statement from the American Heart Association. Circulation. 2020;142(17):e265-e286. doi: 10.1161/CIR.0000000000000920 [DOI] [PubMed] [Google Scholar]
11.Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group . KDIGO 2020 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 2020;98(4S):S1-S115. [DOI] [PubMed] [Google Scholar]
12.Handelsman Y, Anderson JE, Bakris GL, et al. DCRM multispecialty practice recommendations for the management of diabetes, cardiorenal, and metabolic diseases. J Diabetes Complications. 2022;36(2):108101. doi: 10.1016/j.jdiacomp.2021.108101 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Virani SS, Alonso A, Benjamin EJ, et al. ; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee . Heart disease and stroke statistics—2020 update: a report from the American Heart Association. Circulation. 2020;141(9):e139-e596. [DOI] [PubMed] [Google Scholar]
14.United States Renal Data System . 2020 Annual data report. National Institutes of Health. Accessed May 24, 2021. https://usrds-adr.niddk.nih.gov/2020/chronic-kidney-disease/1-ckd-in-the-general-population
15.Centers for Disease Control and Prevention . National diabetes statistics report 2020—estimates of diabetes and its burden in the United States. Accessed May 24, 2021. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
16.Chen TC, Clark J, Riddles MK, Mohadjer LK, Fakhouri THI. National Health and Nutrition Examination Survey, 2015-2018: sample design and estimation procedures. Vital Health Stat 2. 2020;(184):1-35. [PubMed] [Google Scholar]
17.National Center for Health Statistics . National Health and Nutrition Examination Survey—2015-2016 data documentation, codebook, and frequencies—cardiovascular health (CDQ_I). Published September 2017. Accessed May 24, 2021. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/CDQ_I.htm
18.Stevens PE, Levin A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members . Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825-830. doi: 10.7326/0003-4819-158-11-201306040-00007 [DOI] [PubMed] [Google Scholar]
19.Inker LA, Eneanya ND, Coresh J, et al. ; Chronic Kidney Disease Epidemiology Collaboration . New creatinine- and cystatin C–based equations to estimate GFR without race. N Engl J Med. 2021;385(19):1737-1749. doi: 10.1056/NEJMoa2102953 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Mosslemi M, Park HL, McLaren CE, Wong ND. A treatment-based algorithm for identification of diabetes type in the National Health and Nutrition Examination Survey. Cardiovasc Endocrinol Metab. 2020;9(1):9-16. doi: 10.1097/XCE.0000000000000189 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Sarafidis PA, Whaley-Connell A, Sowers JR, Bakris GL. Cardiometabolic syndrome and chronic kidney disease: what is the link? J Cardiometab Syndr. 2006;1(1):58-65. doi: 10.1111/j.0197-3118.2006.05470.x [DOI] [PubMed] [Google Scholar]
22.National Center for Health Statistics . National Health and Nutrition Examination Survey: analytic guidelines, 2011-2014 and 2015-2016. Published December 14, 2018. Accessed May 24, 2021. https://wwwn.cdc.gov/nchs/data/nhanes/analyticguidelines/11-16-analytic-guidelines.pdf
23.Lewis TH. Complex Survey Data Analysis With SAS. CRC Press; 2017. [Google Scholar]
24.Gregg EW, Sorlie P, Paulose-Ram R, et al. ; 1999-2000 National Health and Nutrition Examination Survey . Prevalence of lower-extremity disease in the US adult population ≥40 years of age with and without diabetes: 1999-2000 national health and nutrition examination survey. Diabetes Care. 2004;27(7):1591-1597. doi: 10.2337/diacare.27.7.1591 [DOI] [PubMed] [Google Scholar]
25.Mirel LB, Burt V, Curtin LR, Zhang C. Different approaches for non-response adjustments to statistical weights in the continuous NHANES (2003-04). Accessed February 20, 2023. https://nces.ed.gov/FCSM/pdf/2009FCSM_Mirel_V-B.pdf
26.United States Census Bureau . 2020: ACS 5-year estimates subject tables (S0101: age and sex). Accessed May 2, 2022. https://data.census.gov/cedsci/table?q=S0101&tid=ACSST5Y2020.S0101
27.Rahimi K, Lam CSP, Steinhubl S. Cardiovascular disease and multimorbidity: a call for interdisciplinary research and personalized cardiovascular care. PLoS Med. 2018;15(3):e1002545. doi: 10.1371/journal.pmed.1002545 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Johnson NB, Hayes LD, Brown K, Hoo EC, Ethier KA; Centers for Disease Control and Prevention (CDC) . CDC national health report: leading causes of morbidity and mortality and associated behavioral risk and protective factors—United States, 2005-2013. MMWR Suppl. 2014;63(4):3-27. [PubMed] [Google Scholar]
29.Xu J, Murphy SL, Kochanek KD, Arias E. Mortality in the United States, 2015. NCHS Data Brief. 2016;(267):1-8. [PubMed] [Google Scholar]
30.Heidenreich PA, Albert NM, Allen LA, et al. ; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council . Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606-619. doi: 10.1161/HHF.0b013e318291329a [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Tsao CW, Aday AW, Almarzooq ZI, et al. Heart disease and stroke statistics—2022 update: a report from the American Heart Association. Circulation. 2022;145(8):e153-e639. [DOI] [PubMed] [Google Scholar]
32.Centers for Disease Control and Prevention . Chronic Kidney Disease in the United States, 2021. US Dept of Health and Human Services, Centers for Disease Control and Prevention; 2021. [Google Scholar]
33.Centers for Disease Control and Prevention . Overweight & obesity. Updated September 27, 2022. Accessed May 24, 2021. https://www.cdc.gov/obesity/index.html
34.Ellison-Barnes A, Johnson S, Gudzune K. Trends in obesity prevalence among adults aged 18 through 25 years, 1976-2018. JAMA. 2021;326(20):2073-2074. doi: 10.1001/jama.2021.16685 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. JAMA. 2018;319(16):1723-1725. doi: 10.1001/jama.2018.3060 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.He J, Zhu Z, Bundy JD, Dorans KS, Chen J, Hamm LL. Trends in cardiovascular risk factors in US adults by race and ethnicity and socioeconomic status, 1999-2018. JAMA. 2021;326(13):1286-1298. doi: 10.1001/jama.2021.15187 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Ogden CL, Fryar CD, Martin CB, et al. Trends in obesity prevalence by race and Hispanic origin—1999-2000 to 2017-2018. JAMA. 2020;324(12):1208-1210. doi: 10.1001/jama.2020.14590 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.United Nations, Department of Economic and Social Affairs . World population prospects 2022. Accessed April 2, 2022. https://population.un.org/wpp/Graphs/Probabilistic/PopPerc/65plus/1503
39.Lawson CA, Seidu S, Zaccardi F, et al. Outcome trends in people with heart failure, type 2 diabetes mellitus and chronic kidney disease in the UK over twenty years. EClinicalMedicine. 2021;32:100739. doi: 10.1016/j.eclinm.2021.100739 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Vaduganathan M, Fonarow GC, Greene SJ, et al. Contemporary treatment patterns and clinical outcomes of comorbid diabetes mellitus and HFrEF: the CHAMP-HF Registry. JACC Heart Fail. 2020;8(6):469-480. doi: 10.1016/j.jchf.2019.12.015 [DOI] [PubMed] [Google Scholar]
41.Kazemian P, Shebl FM, McCann N, Walensky RP, Wexler DJ. Evaluation of the cascade of diabetes care in the United States, 2005-2016. JAMA Intern Med. 2019;179(10):1376-1385. doi: 10.1001/jamainternmed.2019.2396 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Patel N, Bhargava A, Kalra R, et al. Trends in lipid, lipoproteins, and statin use among US adults: impact of 2013 cholesterol guidelines. J Am Coll Cardiol. 2019;74(20):2525-2528. doi: 10.1016/j.jacc.2019.09.026 [DOI] [PubMed] [Google Scholar]
43.Wong ND, Young D, Zhao Y, et al. Prevalence of the American College of Cardiology/American Heart Association statin eligibility groups, statin use, and low-density lipoprotein cholesterol control in US adults using the National Health and Nutrition Examination Survey 2011-2012. J Clin Lipidol. 2016;10(5):1109-1118. doi: 10.1016/j.jacl.2016.06.011 [DOI] [PubMed] [Google Scholar]
44.Eberly LA, Yang L, Eneanya ND, et al. Association of race/ethnicity, gender, and socioeconomic status with sodium-glucose cotransporter 2 inhibitor use among patients with diabetes in the US. JAMA Netw Open. 2021;4(4):e216139. doi: 10.1001/jamanetworkopen.2021.6139 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Sangha V, Lipska K, Lin Z, et al. Patterns of prescribing sodium-glucose cotransporter-2 inhibitors for Medicare beneficiaries in the United States. Circ Cardiovasc Qual Outcomes. 2021;14(12):e008381. doi: 10.1161/CIRCOUTCOMES.121.008381 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Vaduganathan M, Sathiyakumar V, Singh A, et al. Prescriber patterns of SGLT2i after expansions of US Food and Drug Administration labeling. J Am Coll Cardiol. 2018;72(25):3370-3372. doi: 10.1016/j.jacc.2018.08.2202 [DOI] [PubMed] [Google Scholar]
47.Zhuo M, Li J, Buckley LF, et al. Prescribing patterns of sodium-glucose cotransporter-2 inhibitors in patients with CKD: a cross-sectional registry analysis. Kidney360. 2022;3(3):455-464. doi: 10.34067/KID.0007862021 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.McCarthy CP, Chang LS, Vaduganathan M. Training the next generation of cardiometabolic specialists. J Am Coll Cardiol. 2020;75(10):1233-1237. doi: 10.1016/j.jacc.2020.02.017 [DOI] [PubMed] [Google Scholar]
49.Chow CK, Ramasundarahettige C, Hu W, et al. ; PURE Investigators . Availability and affordability of essential medicines for diabetes across high-income, middle-income, and low-income countries: a prospective epidemiological study. Lancet Diabetes Endocrinol. 2018;6(10):798-808. doi: 10.1016/S2213-8587(18)30233-X [DOI] [PubMed] [Google Scholar]
50.Marcus ME, Manne-Goehler J, Theilmann M, et al. Use of statins for the prevention of cardiovascular disease in 41 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data. Lancet Glob Health. 2022;10(3):e369-e379. doi: 10.1016/S2214-109X(21)00551-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Afshin A, Forouzanfar MH, Reitsma MB, et al. ; GBD 2015 Obesity Collaborators . Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13-27. doi: 10.1056/NEJMoa1614362 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Després JP, Carpentier AC, Tchernof A, Neeland IJ, Poirier P. Management of obesity in cardiovascular practice: JACC focus seminar. J Am Coll Cardiol. 2021;78(5):513-531. doi: 10.1016/j.jacc.2021.05.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Powell-Wiley TM, Poirier P, Burke LE, et al. ; American Heart Association Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council . Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2021;143(21):e984-e1010. doi: 10.1161/CIR.0000000000000973 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Riaz H, Khan MS, Siddiqi TJ, et al. Association between obesity and cardiovascular outcomes: a systematic review and meta-analysis of Mendelian randomization studies. JAMA Netw Open. 2018;1(7):e183788. doi: 10.1001/jamanetworkopen.2018.3788 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Stenvinkel P, Zoccali C, Ikizler TA. Obesity in CKD—what should nephrologists know? J Am Soc Nephrol. 2013;24(11):1727-1736. doi: 10.1681/ASN.2013040330 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Yarnoff BO, Hoerger TJ, Shrestha SS, et al. ; CRIC Study Investigators . Modeling the impact of obesity on the lifetime risk of chronic kidney disease in the United States using updated estimates of GFR progression from the CRIC study. PLoS One. 2018;13(10):e0205530. doi: 10.1371/journal.pone.0205530 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Kivimäki M, Strandberg T, Pentti J, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol. 2022;10(4):253-263. doi: 10.1016/S2213-8587(22)00033-X [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15(5):288-298. doi: 10.1038/s41574-019-0176-8 [DOI] [PubMed] [Google Scholar]
59.González-Muniesa P, Mártinez-González MA, Hu FB, et al. Obesity. Nat Rev Dis Primers. 2017;3:17034. doi: 10.1038/nrdp.2017.34 [DOI] [PubMed] [Google Scholar]
60.Yanovski SZ, Yanovski JA. Progress in pharmacotherapy for obesity. JAMA. 2021;326(2):129-130. doi: 10.1001/jama.2021.9486 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.MacEwan J, Kan H, Chiu K, Poon JL, Shinde S, Ahmad NN. Antiobesity medication use among overweight and obese adults in the United States: 2015-2018. Endocr Pract. 2021;27(11):1139-1148. doi: 10.1016/j.eprac.2021.07.004 [DOI] [PubMed] [Google Scholar]
62.Saxon DR, Iwamoto SJ, Mettenbrink CJ, et al. Antiobesity medication use in 2.2 million adults across eight large health care organizations: 2009-2015. Obesity (Silver Spring). 2019;27(12):1975-1981. doi: 10.1002/oby.22581 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Zhang S, Manne S, Lin J, Yang J. Characteristics of patients potentially eligible for pharmacotherapy for weight loss in primary care practice in the United States. Obes Sci Pract. 2016;2(2):104-114. doi: 10.1002/osp4.46 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Lavie CJ, Laddu D, Arena R, Ortega FB, Alpert MA, Kushner RF. Healthy weight and obesity prevention: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72(13):1506-1531. doi: 10.1016/j.jacc.2018.08.1037 [DOI] [PubMed] [Google Scholar]
65.Wang L, Li X, Wang Z, et al. Trends in prevalence of diabetes and control of risk factors in diabetes among US adults, 1999-2018. JAMA. 2021;326(8):1-13. doi: 10.1001/jama.2021.9883 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Eberly LA, Yang L, Essien UR, et al. Racial, ethnic, and socioeconomic inequities in glucagon-like peptide-1 receptor agonist use among patients with diabetes in the US. JAMA Health Forum. 2021;2(12):e214182. doi: 10.1001/jamahealthforum.2021.4182 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Yao X, Shah ND, Gersh BJ, Lopez-Jimenez F, Noseworthy PA. Assessment of trends in statin therapy for secondary prevention of atherosclerotic cardiovascular disease in US adults from 2007 to 2016. JAMA Netw Open. 2020;3(11):e2025505. doi: 10.1001/jamanetworkopen.2020.25505 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Shah NS, Lloyd-Jones DM, O’Flaherty M, et al. Trends in cardiometabolic mortality in the United States, 1999-2017. JAMA. 2019;322(8):780-782. doi: 10.1001/jama.2019.9161 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Sidney S, Quesenberry CP Jr, Jaffe MG, et al. Recent trends in cardiovascular mortality in the United States and public health goals. JAMA Cardiol. 2016;1(5):594-599. doi: 10.1001/jamacardio.2016.1326 [DOI] [PubMed] [Google Scholar]
70.Vart P, Powe NR, McCulloch CE, et al. ; Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team . National trends in the prevalence of chronic kidney disease among racial/ethnic and socioeconomic status groups, 1988-2016. JAMA Netw Open. 2020;3(7):e207932. doi: 10.1001/jamanetworkopen.2020.7932 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Khan SU, Lone AN, Yedlapati SH, et al. Cardiovascular disease mortality among Hispanic versus non-Hispanic White adults in the United States, 1999 to 2018. J Am Heart Assoc. 2022;11(7):e022857. doi: 10.1161/JAHA.121.022857 [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Schultz WM, Kelli HM, Lisko JC, et al. Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137(20):2166-2178. doi: 10.1161/CIRCULATIONAHA.117.029652 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10):1021-1029. doi: 10.1001/jama.2015.10029 [DOI] [PubMed] [Google Scholar]

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