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Written by: Elba Medina, MD
An interplay between metabolic syndrome and kidney disease has been known for at least 20 years. The interrelationship between the kidney and the heart, known as "cardiorenal syndrome," was named in 2009. In 2023, The American Heart Association proposed a broader, multidirectional concept that includes the connection between the heart, kidney, and metabolic syndrome called CKM syndrome; this topic is reviewed in a recent KI Reports article.
Figure 2. Infographic adapted from: Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart AssociationPresidential Advisory from the American Heart Association guide on the top 10 highlights of Cardiovascular-Kidney-Metabolic Health for effectively and equitably enhancing Cardiovascular-Kidney-Metabolic health in the population.
The objective of the integration of CKM syndrome was to create a staging system that could be useful for clinical management, prevention, and research purposes. The workgroup developed new risk prediction equations, named predicting risk of CVD events (PREVENT) equations (which included estimated glomerular filtration rate (eGFR) and albuminuria as variables), in addition to acknowledging traditional cardiovascular and metabolic factors.
CKM health stages
Individuals without CKM health risk factors are classified as stage 0. Stages 1 to 4 encompass individuals at risk for CVD due to the presence of metabolic risk factors, hypertension, diabetes, CKD, or a combination of these, as well as individuals with existing CVD. In CKM Stage 3, risk equivalents of subclinical CVD include high-risk CKD (G stage 4 and 5 CKD).
Figure 3. Infographic adapted from: Combination of Cardiovascular, Kidney, and Metabolic Diseases in a Syndrome Named Cardiovascular-Kidney-Metabolic, With New Risk Prediction Equations
Pathophysiology of CKM
A variety of interconnected factors are involved. Among them are insulin resistance, hyperglycemia, stimulation of the renin-angiotensin-aldosterone system, enhanced generation of advanced glycation end-products, oxidative stress, dyslipidemia and lipotoxicity, endoplasmic reticulum stress, mitochondrial dysfunction with impaired cellular energy production, chronic (micro) inflammation, and potentially uremic toxins. The pathophysiological consequences of these processes are numerous and reflect multidirectional relationships among metabolic risk factors (obesity and diabetes), CKD, and the cardiovascular system.
CKM syndrome most commonly originates from excess adipose tissue, dysfunctional adipose tissue, or both. Consequences are not just limited to the known organ systems, but can also affect liver function and the interconnectedness of other organs.
Figure 4. Infographic adapted from The cardio-renal-metabolic connection: a review of the evidence and Combination of Cardiovascular, Kidney, and Metabolic Diseases in a Syndrome Named Cardiovascular-Kidney-Metabolic, With New Risk Prediction Equations
Numerous risk factors cover a diversity of predisposing conditions for CKM syndrome influence its severity as well as related adverse outcomes.
Figure 5. Infographic adapted from Combination of Cardiovascular, Kidney, and Metabolic Diseases in a Syndrome Named Cardiovascular-Kidney-Metabolic, With New Risk Prediction Equations
The pathophysiologic progression of CKM syndrome reflects increasing absolute risk for both cardiovascular disease and kidney failure.
Kidney disease
CKM syndrome includes the 2 major parameters of CKD progression risk: glomerular filtration rate and albuminuria. A meta-analysis by the Writing Group for the CKD Prognosis Consortium showed that a more severe urinary albumin-to-creatinine ratio (uACR) was associated with increased rates of all 10 adverse outcomes. In the KDIGO Guideline, we can observe that these complications increase along with the worsening of eGFR and albuminuria.
Figure 6. Infographic adapted from KDIGO 2024 Clinicial practice guideline for the evaluation and management of chronic kidney disease.
Over the years, risk prediction and risk-based prevention guidelines have been created, published by national or international specialized medical organizations, to improve the prediction and care of entities such as obesity, diabetes, and hypertension and incorporate these into a single tool.
Figure 7. Infographic adapted from Combination of Cardiovascular, Kidney, and Metabolic Diseases in a Syndrome Named Cardiovascular-Kidney-Metabolic, With New Risk Prediction Equations
The ten CVD risk factors include unhealthy dietary intake, physical inactivity, dyslipidemia, pre-diabetes/diabetes, high blood pressure, obesity, considerations of select populations (older age, race/ethnicity, and sex differences), thrombosis (with smoking as a potential contributor to thrombosis), kidney dysfunction and genetics/familial hypercholesterolemia. Several heart failure risk scores were developed to predict 10-year risk of new-onset heart failure in the general population eg.
Ten-year ASCVD risk for those 40 – 75 years of age can be assessed by inputting 10 CVD risk factors into the ACC/AHA ASCVD Risk Calculator found at ASCVD Risk Estimator + (acc.org)
It was considered necessary to develop new risk prediction equations that included eGFR and albuminuria. In addition, changes in traditional cardiovascular and metabolic variables, due to decreases in the prevalence of tobacco use and serum lipid levels and more widespread use of antihypertensive agents, lead to the overestimation of atherosclerotic CVD incidence with pooled cohort equations.
New risk prediction equations, named Predicting Risk of CVD EVENTs (PREVENT) equations, have now been developed. They originated with a derivation and validation cohort from a sample of >6 million people that provided CVD risk of total CVD (and CVD subtypes) estimates over periods of 10 and 30 years.
This information was obtained from electronic medical records-based data sets of patients that:
Were US adults aged 30 to 79 years
Without known CVD
Sex-specific equations
Race-free model
Data have information on 5 key risk factors of interest: systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, BMI, and eGFR.
Estimated glomerular filtration rate (eGFR) as a risk predictor
Additionally, models included:
Kidney: uACR
Metabolic: Hemoglobin A1c
Social: Social determinants of health
Figure 8. From Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association.CVD indicates cardiovascular disease; PREVENT, AHA Predicting Risk of CVD Events; SDI, social deprivation index; SDOH, social determinants of health; and UACR urine albumin-to-creatinine ratio.
The risk model demonstrates good discrimination and calibration in the overall population and among demographic and cardiovascular-kidney-metabolic subgroups (eg, obesity, diabetes, and chronic kidney disease).
This app should be used for primary prevention patients (those without atherosclerotic cardiovascular disease or heart failure) only.
Knowing the result of risk may assist and guide clinicians and patients in shared decision-making for interventions targeting lifestyle behaviors and consideration of pharmacotherapies. This will translate into earlier, more appropriate treatment and prevention of CKM factors in clinical practice.
Notwithstanding the benefits PREVENT equations, were observed several limitations:
1 ) The authors used electronic medical records/based data sets
2) Excluded people with extreme clinical values of systolic blood pressure, serum total, and HDL cholesterol, or BMI,
3) The long baseline time period of the included data sets, spanning more than 3v decades, might have led to differences in risk factor prevalence and treatment modalities,
4) The authors used age as the time scale for model development as the time scale.
5) Individual-level social determinants of health were not routinely available in all data sets
6) The PREVENT model development did not include a variety of well-known biomarkers of target organ damage.
7) Separate modeling was used for total CVD and its components in the development of PREVENT equations.
Other potential limitations were: Valid only for individuals aged 35 to 79 years in the US, it's necessary to be validated for younger people and ethnic groups living outside North America, and another limitation is the use of BMI as the sole obesity measure.
Conclusion
The primary goal is prevention starting with the root causes of CKM syndrome. Once CKM develops, fragmented care amongst multiple specialties can be a challenge for individuals with multiple comorbid conditions. Care needs to be integrated between specialties and holistic with considerations towards social determinants of health.
The proposed staging system represents a qualitative approach to assessing risk and slowing disease progression. If the patient meets any two out of three CKM criteria (diabetes, cardiovascular disease, or kidney disease), they should be funneled into a multidisciplinary care team including the appropriate sub-specialties and a clinical care coordinator (value-base, volume-base).
CKM syndrome is a health disorder due to interactions among heart disease, kidney disease, diabetes, and obesity. The clinical presentation is heterogeneous; yet early recognition and treatment, guided by risk prediction tools, provide a chance to change the trajectory of poor outcomes and premature death; notwithstanding, it's necessary to improve the risk prediction tools, considering their limitations, and create more specific recommendations based on the score's results.
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