Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases

doi:10.21203/rs.3.rs-4987225/v1

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Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases

https://doi.org/10.21203/rs.3.rs-4987225/v1

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Background

The American Heart Association (AHA) has introduced the cardiovascular-kidney-metabolic (CKM) syndrome as a systemic health disorder affecting nearly all organ systems. However, evidence supporting CKM syndrome health-related effects remains scarce, and its effects beyond its components are not studied.

Methods

Here, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of CKM syndrome with adverse health outcomes, including functional disability, physical frailty, falls, hospitalization and mortality. Then, we examined the associations between CKM syndrome and all-cause and cause-specific mortalityusing data from the US National Health and Nutrition Examination Survey (NHANES).

Results

CKM syndrome stages were independently associated with incident geriatric syndromes, including incident disability (OR, 1.2, [95% CI, 1.11-1.29]), frailty (OR, 1.28, [95% CI, 1.15-1.42]), falls (OR, 1.11, [95% CI, 1.06-1.17]), and hospitalization (OR, 1.19, [95% CI, 1.14-1.25]). Moreover, CKM syndrome stages independently predicted all-cause mortality (CHARLS: HR, 1.12 [95% CI, 1.06-1.18]; NHANES: HR, 1.34 [95% CI, 1.29-1.40]), and cause-specific mortality, including CVD (HR, 1.62, [95% CI, 1.50-1.75]), malignant neoplasm (HR, 1.20, [95% CI, 1.10-1.30]), respiratory disease (HR, 1.26, [95% CI, 1.07-1.49]). After adjusting for CKM components, the positive associations of CKM syndrome with geriatric syndrome and mortality were persisted, indicating that CKM syndrome has health implications beyond those of its component disorders. There were dose-effect relationships between CKM stages and risks of geriatric syndromes and mortality. Especially, advanced CKM syndrome (stage 3-4) exhibited higher risks of adverse outcomes along the CKM staging spectrum.

Conclusion

The findings revealed that CKM syndrome significantly impacted the overall health among middle-aged and older adults, advocating its application from CVD to geriatric community and multidiscipline clinical settings. It suggested the need for CKM syndrome assessment to predict and prevent geriatric syndromes and major chronic conditions, thereby reducing social and healthcare burdens. Moreover, these results provided empirical evidence for the validity and robustness of the CKM staging classification.

Health sciences/Diseases/Cardiovascular diseases

Health sciences/Diseases/Endocrine system and metabolic diseases

Health sciences/Health care/Public health

CKM syndrome

CKM staging construct

disability

frailty

mortality

Question

What’s the cardiovascular-kidney-metabolic syndrome health-related effects on geriatric syndrome and risks of mortality?

Findings

In this cohort study involving 21920 participants at baseline with 4028 deaths, and 672, 346, 1766, 1834 incident cases of disability, physical frailty, falls and hospitalization during follow-up periods, CKM syndrome was significantly associated with increased risks of these geriatric syndromes, all-cause mortality and cause-specific mortality, including cardiovascular disease (CVD), cancer, respiratory disease, and other causes.

Meaning

The findings supported the American Heart Association's proposal that CKM syndrome manifested as a systemic health disorder impacting overall health, advocating CKM application beyond CVD to the geriatric community and multidiscipline clinical settings.

The historically high prevalence of obesity and diabetes has become a significant social burden, impacting a transition from affecting primarily adults to now affecting youth from an early age^1,2. These metabolic risk factors, including obesity and diabetes, are associated with increased risks of cardiovascular disease (CVD) events^3–5. It’s projected that CVD will impact over 180 million adults (about 60%) in the US in the coming decades, leading to substantial rises in healthcare costs and productivity losses⁶. Based on both social needs and medical rationale, the American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome⁷, arising from the connections among obesity, diabetes, chronic kidney disease (CKD), and CVD. To improve CKM health and related outcomes, the CKM staging construct was proposed to rationally categorize individuals at risk for or affected by CVD, aiding in screening and risk assessment. According to AHA, CKM syndrome manifested as a holistic and systemic health disorder that impacted nearly all organ systems and contributed to multiorgan diseases⁷. Despite its recent proposal, evidence supporting CKM syndrome health-related effects remains scarce, and its effects beyond its components are not studied.

Emerging evidences suggest significant association between CKM syndrome components and risks of geriatric syndromes and major chronic diseases (e.g., CVD, cancer, respiratory disease). CKM syndrome tended to progressed with age in parallel with the progression of geriatric syndromes^8,9. Moreover, the components of CKM syndrome, such as abdominal obesity^10,11, metabolic syndrome^12,13, diabetes^14,15, hypertension¹⁶, higher cardiovascular risk burden^17,18, and impaired renal function^19–21 increased the risks of geriatric syndromes (e.g., functional disability, frailty). Within the CKM staging construct, CKM risk factors (such as obesity⁴, diabetes⁵, and metabolic syndrome²²) were linked to increased risks of CVD. In addition, the CKM syndrome components were also associated non-CVD outcomes. For instance, metabolic syndrome increased risk of cognitive decline²³, nonalcoholic fatty liver disease²⁴, kidney disease²⁵, and cancer²⁶. This evidence underscored that CKM syndrome might increase the risks of geriatric syndromes and chronic diseases. Therefore, we aimed to provide evidence for broader application of CKM syndrome in multidisciplinary clinical and community settings, particularly within geriatric medicine and community.

Here, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of CKM syndrome with incident geriatric syndromes and mortality. These geriatric syndromes included functional disability, physical frailty, falls, and hospitalization, which were considered as serious, intermediate, accidental, and healthcare resource utilization-related outcomes. Then, we examined the association between CKM syndrome and all-cause and cause-specific mortality using data from the US National Health and Nutrition Examination Survey (NHANES).

Study populations

The CHARLS is an ongoing prospective population-based longitudinal cohort study of middle-aged and older Chinese adults²⁷. The baseline survey was conducted between June 2011 and March 2012, and the participants were followed up biennially until 2020. The 2011 wave was taken as baseline, and 9389 subjects (aged 45–85 years) with routine clinical tests were enrolled. CHARLS was approved by the Ethics Review Board of Peking University, which was conducted in accordance with the Declaration of Helsinki and other relevant guidelines and regulations. The US NHANES is a nationally representative cross-sectional survey of civilian, noninstitutionalized persons living in the US. We used data from the NHANES 1998–2018, from a publicly available data dictionary²⁸. The demographic information, physical examination results, questionnaire items, and mortality information were collected. The mortality status was linked from the National Death Index. NHANES was approved by the NCHS Ethics Review Board (ERB). All participants provided informed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies.

Biomarkers collection

For CHARLS, the baseline (2011 wave) collected fasting blood samples for complete blood count (CBC) tests and blood-based bioassays, in which 16 biomarkers were measured for the participants. For NHANES, we collected physical, hematological and biochemical biomarkers. For CKM stage evaluation, body mass index (BMI), waist circumstance (WC), systolic and diastolic pressure (SBP, DBP) were collected. Total cholesterol (TC), high-density and low-density lipoprotein cholesterol (HDL, LDL), triglyceride (TG), plasma glucose, glycosylated hemoglobin (HbA1c) and creatinine were measured. Fasting blood glucose (100 mg/dL to < 126 mg/dL) or glycated hemoglobin (5.7% to < 6.5%) were used to diagnose individuals with prediabetes. Estimated glomerular filtration rate (GFR) was calculated using the race-free CKD-EPI 2021 creatinine equation.

Assessment of CKM syndrome

According to the guideline, five stages of CKM syndrome were assessed: stage 0 (no CKM risk factors), stage 1 (excess and/or dysfunctional adiposity), stage 2 (metabolic risk factors and kidney disease), stage 3 (subclinical CVD) and stage 4 (clinical CVD)²⁹. In detail, CKM Stage 0 included participants with normal BMI (< 23 kg/m2) and normal WC (< 80 for women and < 90 cm for men among Asians, respectively). Participants meet criteria for the other stages were diagnosed as higher stages. CKM Stage 1 identified individuals with elevated BMI (≥ 23 kg/m2), or elevated WC (≥ 80 and ≥ 90 cm for women and men, respectively), or prediabetes. CKM Stage 2 identified participants with metabolic risk factors or moderate-to-high-risk CKD. metabolic risk factors included elevated fasting serum triglycerides (≥ 135 mg/dL), hypertension, diabetes, or metabolic syndrome. The metabolic syndrome was evaluated through WC, HDL, TG, blood pressure, and prediabetes, in which individuals with more than three abnormalities. CKM Stage 3 was identified based of the presence of very-high-risk KDIGO CKD stages, or a self-reported chronic kidney disease, or a high-predicted 10-year CVD risk (≥ 20%). The 10-year cardiovascular risk was estimated with the AHA Predicting Risk of CVD EVENTs (PREVENT) equations³⁰. The calculation details of the PREVENT were described in Supplementary. CKM Stage 5 was identified based on self-reported established cardiovascular disease (coronary heart disease, angina, heart attack, heart failure, and stroke).

Assessment of geriatric syndromes

In CHARLS, six items of daily self-care ability were collected from each participant based on the Katz index³¹, including dressing, bathing, transferring from a chair to bed, toilet use, feeding and walking. These items were asked with four responses, i.e., “No, I do not have any difficulty”, “I have difficulty but still can do it”, “Yes, I have difficulty and need help” and “I cannot do it”. As reported in previous studies, ADL disability was defined if the participants have difficulty to perform at least one of 6 daily self-care tasks.

Frailty was evaluated through the widely used criteria originally proposed by Fried et al.³², modified with the information available in the CHARLS³³. In the definition, frailty contained five components of exhaustion, weakness, low physical activity, weight loss, and slowness. In our study, the five components of frailty were evaluated and defined as follows: (1) Exhaustion: exhaust was defined according to the Chinese version of the Center for Epidemiologic Studies-Depression scale (CES-D) questions (20): “I felt everything I did was an effort during last week” or “I could not get going during last week.”. Exhaustion was present if the participants answered “Most or all of the time” or “Occasionally or a moderate amount of the time” for the either question. (2) Weakness: weakness was measured using the question “having difficulty in Lifting or carrying weights over 10 jin, like a heavy bag of groceries”. (3) Low physical activity: CHARLS defined WALK as walking those participants might do solely for recreation, sport, excise, or leisure at work and at home, walking to travel from place to place, and any other walking. Low physical activity was considered to be present if the participants had no physical activity or WALK at least 10 min at a time during a usual week. (4) Weight loss: the weight loss was defined as the unintentional loss of 5 or more kilograms in the last year or current body mass index (BMI) ≤ 18.5 kg/m2. (5) Slowness: these questions asked the participants to report whether they have difficulty with walking 100 meters or climbing several flights of stairs without resting. Those participants who had difficulty with performing the walking or climbing were categorized as slowness. In our study, robustness was defined as the absence of any component, pre-frailty as the presence of one or two of these components, and frailty as the presence of three or more components.

Information of falls, and hospitalization were obtained through questionnaire at the biennial follow-ups. These geriatric syndromes, including functional disability, physical frailty, falls and hospitalization were evaluated at the baseline (2011 wave) and the follow-ups (2013 and 2015 waves). Individuals identified as ADL disability and frailty, or recorded with history of falls and hospitalization were excluded in association analysis.

Assessment of adverse outcomes and mortality

In CHARLS, records on death were obtained at the biennial follow-up at the 2011, 2013, 2015, 2018 and 2020 waves. Because the exact date of death was not available, the midpoint of the two follow-up visits was defined as the time of death. In US NHANES, dates and causes of death were linked to the National Death Index records to 31 December 2019. We used the 10th revision of the International Statistical Classification of Diseases (ICD-10) to identify causes of death. Mortality outcomes in this study included the following underlying causes of death: including heart disease, Malignant neoplasms, respiratory disease, cerebrovascular disease, Alzheimer’s disease, diabetes, influenza/pneumonia, and nephritis/nephrosis.

Statistical analysis

First, continuous and categorical data are presented as the means and standard deviations (SDs) or frequencies (%), respectively. Second, we utilized logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of CKM syndrome stages associated with frailty, disability, falls, hospitalization, using two models. Model 1: crude model; Model 2: adjusted for age (continuous), gender, education, race, smoking status, alcohol consumption status, BMI (continuous), cancer. Model 3: covariates in model 2 and CKM components (hypertension, diabetes, heart disease, stroke); Cox proportional hazard models were employed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) in the associations between CKM syndrome stages and mortality, with crude and adjusted models. We also conducted Kaplan‒Meier survival analysis to plot the associations of CKM syndrome stages with all-cause mortality. All results were considered significant at a P value < 0.05 (2-tailed). All analyses were conducted using R statistical software (version 4.2.1; www.r-project.org).

Characteristics of the study population

Table 1 shows the characteristics of the study population in the CHARLS and NHANES. The baseline demographic characteristics, lifestyle factors, laboratory biomarkers and chronic diseases of the study population are presented in detail. At baseline, 9389 participants (72.1% aged 45–64 years and 27.9% 65–85 years; 46.7% male) in the CHARLS, and 12531 participants (58.3% aged 45–64 years and 41.7% aged 65–85 years; 49.7% male) in the NHANES were analyzed. The mean ages of the participants were 49.4 (9.2) years in the CHARLS and 62.5 (11.1) in the NHANES. In the CHARLS, the overall prevalence of CKM syndrome from stage 0 to 4 were 11.4%, 23.1%, 39.4%, 12.5% and 13.6%, respectively. Correspondingly, in the NHANES, the prevalence of CKM syndrome stages were 4.6%, 17.9%, 46.8%, 13.6% and 17.1%, respectively.

Table 1

Characteristic of Study Population in CHARLS and NHANES.
Demographic	CHARLS (N = 9389)	NHANES (N = 12531)
Age, year, M (SD)	59.4 (9.2)	62.5 (11.1)
45–64, years	6774 (72.1)	7307 (58.3)
65–85, years	2615 (27.9)	5224 (41.7)
Sex, n (%)
Male	4381 (46.7)	6231 (49.7)
Female	5008 (53.3)	6300 (50.3)
Race and ethnicity
White		6109 (48.8)
Black		2372 (18.9)
Hispanic		3060 (24.4)
Asian, Other	9389 (100.0)	990 (7.9)
Education
Less than high school	8474 (90.3)	3731 (29.8)
High school or equivalent	789 (8.4)	2887 (23.1)
College or above	123 (1.3)	5897 (47.1)
WC, cm, M (SD)	84.3 (12.6)	101 (14.8)
BMI, kg/m², M (SD)	23.5 (3.8)	29.1 (6.2)
SBP, mmHg, M (SD)	131 (22.1)	130.8 (20.4)
DBP, mmHg, M (SD)	75.9 (12.2)	69.9 (14.1)
Current smoking, n (%)	2851 (77.3)	2276 (18.2)
Current drinking, n (%)	2334 (24.9)	8439 (70.4)
Glucose, mg/dL, M (SD)	110.6 (37.6)	114.8 (39.5)
HbAc1, %, M (SD)	5.3 (0.8)	6 (1.2)
TC, mg/dL, M (SD)	193.7 (38.7)	200.1 (43.1)
HDL, mg/dL, M (SD)	51.2 (15.3)	54.5 (16.6)
LDL, mg/dL, M (SD)	116.6 (35.1)	118.4 (36.8)
TG, mg/dL, M (SD)	133.5 (106.2)	139.6 (113.9)
Creatine, mg/dL, M (SD)	0.8 (0.2)	0.9 (0.4)
Chronic conditions
Hypertension, n (%)	2365 (25.2)	6202 (49.5)
Diabetes, n (%)	543 (5.8)	2258 (18)
Cancer, n (%)	95 (1)	1759 (14)
Heart disease, n (%)	1121 (11.9)	1714 (13.7)
Stroke, n (%)	629 (6.7)	698 (5.6)
CKM stages
Stage 0	1066 (11.4)	579 (4.6)
Stage 1	2172 (23.1)	2265 (18.1)
Stage 2	3702 (39.4)	5839 (46.6)
Stage 3	1176 (12.5)	1706 (13.6)
Stage 4	1273 (13.6)	2142 (17.1)

Association of CKM stages with frailty, disability, falls and hospitalization

The CHARLS documented 672, 346, 1766, 1834 incident cases of ADL disability, physical frailty, falls and hospitalization, respectively. After adjusting for covariates (socioeconomic status, lifestyle factors, etc.), elevated CKM syndrome stages were also associated with increased risk of incident disability (OR, 1.2, [95% CI, 1.11–1.29]), frailty (OR, 1.28, [95% CI, 1.15–1.42]), falls (OR, 1.11, [95% CI, 1.06–1.17]), and hospitalization (OR, 1.19, [95% CI, 1.14–1.25]). Then, the CKM components, including hypertension, diabetes, stroke, and heart disease, were adjusted. Positive associations were also found between CKM stages and incident disability (OR, 1.2 [95% CI, 1.07–1.36]), frailty (OR, 1.18 1.2 [95% CI, 1.01–1.38]), falls (OR, 1.09 [95% CI, 1.02–1.17]) and hospitalization (OR, 1.08 [95% CI, 1.01–1.16]). Compared with participants in the CKM syndrome stage 0, elevated CKM stages showed increasing odds ratios along the CKM staging spectrum (P < 0.05). Especially, participants categorized as advanced CKM syndrome (stage 3–4) exhibited higher risks of adverse outcomes, presenting a 38–140% increase in risk (all P < 0.05). These results suggested dose-effect relationships between CKM stages and various geriatric syndromes.

Association of CKM stages with mortality

The CHARLS documented 1186 deaths over a median follow-up of 9 years, and the NHANES documented 2842 deaths over a median follow-up of 8.4 years. The results of Cox proportional hazard analyses regarding the association of continuous CKM syndrome stages with the risk of all-cause mortality are presented in Table 2. After adjusting for covariates (Model 2), elevated CKM syndrome stages were associated with all-cause mortality (CHARLS: HR, 1.12 [95% CI, 1.06–1.18]; NHANES: HR, 1.35 [95% CI, 1.29–1.40]). Then, adjusting for CKM components (Model 3), positive associations were also found between CKM stages and all-cause mortality in NHANES (HR, 1.23 [95% CI, 1.15–1.31]), not CHARLS (HR, 1.06 [95% CI, 0.98–1.14]). Compared with participants in the CKM syndrome stage 0, those in the advanced stages experienced significantly greater risks of mortality (e.g., in CHARLS, stage 3: HR, 2.01 [95% CI, 1.44–2.82], stage 4: HR, 2.45 [95% CI, 1.85–3.24]; in NHANES, stage 3: HR, 2.10 [95% CI, 1.49–2.96], stage 4: HR, 2.54 [95% CI, 1.91–3.38]). The dose-effect relationships also presented between CKM stages and all-cause mortality in CHARLS and NHANES (P < 0.001).

Table 2

Associations of CKM syndrome with geriatric syndromes.
		Model 1		Model 2
CKM stages	Events/N	OR (95% CI)	P	OR (95% CI)	P
Disability
Continuous	672/3116	1.27 (1.18–1.36)	< 0.001	1.2 (1.11–1.29)	< 0.001
Stage 0	52/322	Ref		Ref
Stage 1	104/643	1 (0.7–1.45)	0.992	1.11 (0.74–1.68)	0.620
Stage 2	242/1217	1.29 (0.93–1.81)	0.130	1.36 (0.93–1.99)	0.112
Stage 3	128/412	2.34 (1.64–3.39)	< 0.001	1.98 (1.26–3.15)	0.003
Stage 4	146/522	2.02 (1.42–2.89)	< 0.001	1.88 (1.23–2.88)	0.003
Frailty
Continuous	346/3469	1.3 (1.18–1.44)	< 0.001	1.28 (1.15–1.42)	< 0.001
Stage 0	31/388	Ref		Ref
Stage 1	62/855	0.9 (0.58–1.43)	0.647	1.01 (0.61–1.69)	0.974
Stage 2	127/1401	1.15 (0.77–1.76)	0.509	1.1 (0.69–1.79)	0.701
Stage 3	58/403	1.94 (1.23–3.1)	0.005	2.17 (1.23–3.84)	0.007
Stage 4	68/422	2.21 (1.42–3.51)	< 0.001	2.4 (1.38–4.2)	0.002
Falls
Continuous	1766/7820	1.14 (1.09–1.19)	< 0.001	1.11 (1.06–1.17)	< 0.001
Stage 0	180/911	Ref		Ref
Stage 1	372/1837	1.03 (0.85–1.26)	0.762	1.01 (0.8–1.26)	0.958
Stage 2	692/3141	1.15 (0.96–1.38)	0.142	1.14 (0.92–1.41)	0.228
Stage 3	243/939	1.42 (1.14–1.77)	0.002	1.38 (1.05–1.82)	0.022
Stage 4	279/992	1.59 (1.28–1.97)	< 0.001	1.58 (1.21–2.07)	< 0.001
Hospitalization
Continuous	1834/8207	1.25 (1.19–1.31)	< 0.001	1.19 (1.14–1.25)	< 0.001
Stage 0	173/979	Ref		Ref
Stage 1	379/1982	1.1 (0.9–1.35)	0.340	1.15 (0.92–1.43)	0.235
Stage 2	703/3315	1.25 (1.05–1.51)	0.016	1.14 (0.92–1.41)	0.235
Stage 3	261/974	1.71 (1.37–2.12)	< 0.001	1.44 (1.09–1.89)	0.009
Stage 4	318/957	2.32 (1.88–2.87)	< 0.001	2.11 (1.62–2.74)	< 0.001
Model1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer.

Table 3

Hazard ratios of all-cause mortality according to CKM.
		Model 1		Model 2
NHANES	Event	HR (95% CI)	P-value	HR (95% CI)	P-value
CHARLS
Continuous	1186/9389	1.35 (1.29–1.42)	< 0.001	1.12 (1.06–1.18)	< 0.001
Stage 0	121/1066	1.00 (ref)
Stage 1	170/2172	0.68 (0.54–0.85)	0.001	0.89 (0.65–1.22)	0.474
Stage 2	324/3702	0.76 (0.62–0.94)	0.012	1.19 (0.92–1.55)	0.187
Stage 3	344/1176	2.89 (2.35–3.56)	< 0.001	2.01 (1.44–2.82)	< 0.001
Stage 4	227/1273	1.63 (1.31–2.03)	< 0.001	2.45 (1.85–3.24)	< 0.001
NHANES
Continuous	2953/12531	1.93 (1.86-2.00)	< 0.001	1.35 (1.29–1.40)	< 0.001
Stage 0	68/579	1.00 (ref)		1.00 (ref)
Stage 1	236/2259	1.12 (0.85–1.46)	0.428	0.97 (0.71–1.33)	0.850
Stage 2	841/5833	1.5 (1.17–1.92)	0.001	1.23 (0.94–1.61)	0.125
Stage 3	882/1703	8.22 (6.40-10.55)	< 0.001	2.10 (1.49–2.96)	< 0.001
Stage 4	926/2141	5.98 (4.67–7.66)	< 0.001	2.54 (1.91–3.38)	< 0.001
Model1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer.

Table 4

Hazard ratios of cause-specific mortality according to continuous CKM stages in NHANES.
		Model 1		Model 2
NHANES	Events	HR (95% CI)	P-value	HR (95% CI)	P-value
Underlying cause
Cardiovascular disease	941	2.50 (2.35–2.66)	< 0.001	1.62 (1.50–1.75)	< 0.001
Malignant neoplasm	712	1.78 (1.66–1.90)	< 0.001	1.19 (1.10–1.29)	< 0.001
Respiratory disease	165	1.93 (1.68–2.23)	< 0.001	1.26 (1.07–1.49)	0.005
Alzheimer’s disease	117	1.95 (1.65–2.31)	< 0.001	1.15 (0.93–1.42)	0.190
Others	1018	2.07 (1.96–2.20)	< 0.001	1.42 (1.33–1.52)	< 0.001
Model1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer.

In the NHANES, after adjusting for covariates (Model 2), elevated CKM syndrome stages were also associated with increased risk of mortality of cardiovascular disease (HR, 1.62, [95% CI, 1.50–1.75]), malignant neoplasm (HR, 1.19, [95% CI, 1.10–1.29]), respiratory disease (HR, 1.26, [95% CI, 1.07–1.49]), and other causes (HR, 1.42, [95% CI, 1.33–1.52]). Taking the CKM syndrome stage 0 as reference, CKM advanced stages were associated with increased risks of cardiovascular disease, malignant neoplasms and other causes of mortality.

In this cohort study, we included participants from CHARLS and NHANES to investigate the associations between CKM syndrome stages and the risks of adverse outcomes among middle-aged and older adults. Our findings indicated that CKM syndrome stages, particularly the advanced stages, were significantly associated with increased risks of geriatric syndromes, including disability, frailty, falls, and hospitalization. In addition, CKM syndrome stages were also associated with cause-specific mortality, including CVD, malignant neoplasm, respiratory disease, and other causes. After adjusting for CKM components, the positive associations retained, indicating that CKM syndrome had health-related impacts beyond those of its component disorders. These results highlighted CKM syndrome as a systemic health condition contributing to multiorgan dysfunctions, underscoring its relevance in gerontology, geriatrics, major chronic diseases and broader multidisciplinary contexts.

Previous studies focused on CKM components consisting of risk factors, rather than the whole construct of CKM syndrome, regarding to geriatric syndromes. Giltay et al. illustrated that CKM components consisting of BMI, hypertension, diabetes, and serum cholesterol, were predictive of functional disability³⁴. Similarly, Cui et al. reported that higher cardiovascular risk burdens, as assessed by the Framingham general cardiovascular risk score, increased the risk of disability and accelerated its progression over time¹⁷. As for frailty, CKM components, including obesity, hypertension, dyslipidemia, diabetes, increased the risk of frailty³⁵. Moreover, cardiovascular disorders, such as stroke and coronary artery disease, were associated with falls³⁶. A consensus was reached to incorporate cardiovascular assessments in falls risk screening and prevention for older adults at risk of falls³⁷. In contrast, our study employed the CKM syndrome construct, encompassing both CVD risk factors and CVD events comprehensively. We found that the CKM stages were associated with increased risks of geriatric syndromes with dose-effect relationships. Therefore, the CKM assessment could refine prediction model for risks stratification of geriatric syndromes. It highlighted the need to promote the concept of CKM staging construct and its education to geriatric community, clinician, and older adults to prevent these geriatric syndromes. Optimizing CKM health could help to enhance the geriatric health conditions and reduce social and healthcare burdens. In addition, these results confirmed the validity and robustness of the CKM staging construct, which enabled researchers and clinicians to categorize the individuals along a continuum of increasing risk in practice.

We found that CKM syndromes predicted cause-specific mortality, including CVD, cancer, respiratory disease and other cause. Prior studies had reported similar associations through CKM components or risk factors³⁸. For instance, Lau et al. reported that a 10-year ASCVD risk score and natriuretic peptide concentrations were associated with increased risk of incident cancer³⁹. Meanwhile, Mercedes et al. demonstrated a cardiovascular disease risk score was strongly associated with cancer mortality, such as lung, breast, and colon cancers⁴⁰. Similarly, respiratory diseases shared CKM risk factors, such as hypertension, dyslipidemias⁴¹, diabetes and metabolic syndrome. In our study, we applied CKM staging construct to explore its associations with both CVD mortality and non-CVD mortality (e.g., cancer, respiratory disease). The CKM stages were linked to CVD mortality with highest risks, especially the advanced stages. It supported the CKM staging construct’s objective to prevent CVD events and mortality from early CKM stages. These associations between CKM syndrome and mortality of major chronic diseases underscored CKM syndrome as a systemic health condition impacting multiorgan systems and overall health. Prediction of the future aging-associated outcomes (e.g., geriatric syndromes, major chronic diseases), and its clinical utility validated the CKM construct and its substantial applications⁴². Screening for each CKM stage could help predict risks of aging-associated, CVD and non-CVD outcomes related to CKM syndrome. These results advocated for the use of CKM assessment in preventing major chronic diseases among middle-aged and older adults.

Our study demonstrated the incident geriatric syndromes and mortality followed advanced CKM stages, marked by subclinical and clinical CVD. Therefore, the primary objective was to prevent geriatric syndromes and major age-related chronic diseases among middle-aged and older adults in advanced CKM stages. Enhanced health care, such as intensified preventive therapies and pharmacological medications, was necessary to prevent CKM progression and mitigate adverse outcomes. The prevalence of early stages and advanced stages of CKM syndrome accounted for approximately 60% and 30% in CHARLS, respectively, highlighting its severe burden in the general population. The CKM staging construct emphasized early detection of CKM-related changes to support prevention efforts. For instance, lifestyle modification and weight loss can address excess or dysfunctional adiposity in early CKM stages.

Within the CKM construct, it recognized the importance of the relationships between kidney health and cardiovascular health. Impaired kidney function was linked with elevated risks of CVD and geriatric syndromes^43,44. Risk prediction equations of kidney function could inform clinical management and prevention of CKM syndrome and geriatric syndrome. Moreover, it started to emphasize the social determinants of health in assessments for CKM risk factors⁷. The social needs, including financial strain, education and mental health, played a critical role in CKM health and healthy aging. Therefore, evaluation of kidney health and social risk factors were critical in risk screening, prediction and prevention of geriatric syndromes.

Several limitations should be noted in this study. Firstly, subclinical CVDs and CVDs were determined by predicted risks and self-reports, respectively, which may introduce recall bias and affect associations. Secondly, we focused on disability, frailty, falls, hospitalization, and mortality as outcomes; further studies should consider other metrics, such as cognitive decline, physical function declines, hip fracture and other functional metrics. Lastly, while cause-specific mortality was assessed, evidence regarding disease incidence remained limited for CKM syndrome.

In conclusion, the findings highlighted CKM syndrome as a systemic health disorder affecting overall health among middle-aged and older adults, advocating for its broader application in multidisciplinary research, diverse clinical and community settings, especially the gerontology research and geriatrics. Application of CKM in geriatrics will enhance geriatric conditions and promote healthy aging to alleviate the social and healthcare burdens. These results provided substantial evidence to support the validity and robustness of the CKM staging construct, and its goal to prevent CVD events and mortality.

Acknowledgments

The data used in this research were obtained from the CHARLS and NHANES. We would like to thank the workers, researchers, and participants involved in the cohorts.

Funding

This work was supported by grants from the National Natural Science Foundation of China (32300533, 82301768, 32100510), the Shanghai Sailing Program (23YF1430500), and Shanghai Municipal Science and Technology Major Project.

Conflict of interest

The authors declare no competing interests.

Data sharing statement

The data from CHARLS is available upon application at http://charls.pku.edu.cn/. The data from the NHANES are available at www.cdc.gov/nchs/nhis/index.htm.

Author contributions

Dr.Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hao, Zhang, Wang and Jin.

Acquisition, analysis, or interpretation of data: Hao and Zhang.

Drafting of the manuscript: Hao, Wang and Zhang.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Hao, Zhang, Li, Hu.

Administrative, technical, or material support: Zhang, Hao, Jiang, Hu and Wang.

Supervision: Wang, Lin, Sun.

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Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases

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Abstract

Key Points

Introduction

Methods

Study populations

Biomarkers collection

Assessment of CKM syndrome

Assessment of geriatric syndromes

Assessment of adverse outcomes and mortality

Statistical analysis

Results

Characteristics of the study population

Association of CKM stages with frailty, disability, falls and hospitalization

Association of CKM stages with mortality

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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