Effect of Mitochondria‐Derived Methylmalonic Acid on Progress and Mortality Risk in US Adults With Cardiovascular‐Kidney‐Metabolic Syndrome

Hongyu Liu; Shuang Wu; Bi Huang; Yang Liu; Ziyi Zhong; Manlin Zhao; Yimeng Wang; Tommaso Bucci; Chunyu Liu; Yang Chen; Kui Hong; Gregory YH Lip

doi:10.1161/JAHA.125.043066

. 2026 Jan 14;15(2):e043066. doi: 10.1161/JAHA.125.043066

Effect of Mitochondria‐Derived Methylmalonic Acid on Progress and Mortality Risk in US Adults With Cardiovascular‐Kidney‐Metabolic Syndrome

Hongyu Liu ^1,^2,^#, Shuang Wu ^3,^4,^#, Bi Huang ^2,⁵, Yang Liu ^6,⁷, Ziyi Zhong ^2,⁸, Manlin Zhao ^2,⁹, Yimeng Wang ^3,⁴, Tommaso Bucci ^2,¹⁰, Chunyu Liu ¹¹, Yang Chen ^2,^12,^✉, Kui Hong ^1,^6,^7,^✉, Gregory YH Lip ^2,^13,^14,^†

^¹Department of Cardiovascular Medicine, The Second Affiliated Hospital, School of Basic Sciences, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China

^²Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK

^³National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

^⁴National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

^⁵Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China

^⁶Department of Genetic Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China

^⁷Jiangxi Provincial Key Laboratory of Molecular Medicine, The Second Affiliated Hospital, Nanchang, Jiangxi, People’s Republic of China

^⁸Department of Musculoskeletal Ageing and Science, Institute of Life Course and Medical Sciences, University of Liverpool, UK

^⁹Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Engineering Research Center of Medical Devices for Cardiovascular Diseases, Ministry of Education, National Clinical Research Center for Cardiovascular Diseases, Beijing, People’s Republic of China

^¹⁰Department of Clinical Internal, Anaesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy

^¹¹The Third Clinical Medical School, The Rehabilitation Medical School of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China

^¹²Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK

^¹³Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

^¹⁴Medical University of Bialystok, Bialystok, Poland

Correspondence to: Kui Hong, PhD, Department of Cardiovascular Medicine, The Second Affiliated Hospital, School of Basic Sciences, Jiangxi Medical College, Nanchang University, No. 1 Minde Road, Donghu District, Nanchang 330006, Jiangxi, China. Email: hongkui88@163.com , Yang Chen, MD, Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, William Henry Duncan Building, 6 West Derby Street, Liverpool L7 8TX, UK. Email: yang.chen2@liverpool.ac.uk

^†

G. Y. H. Lip is senior author.

^✉

Corresponding author.

Contributed equally.

PMCID: PMC12919479 PMID: 41532538

Abstract

Background

Methylmalonic acid (MMA) is regarded a biomarker of aging, oxidative stress, and mitochondrial dysfunction, which are also pathophysiological mechanisms that underlying cardiovascular‐kidney‐metabolic (CKM) syndrome. Nevertheless, the relationship between MMA per se and CKM remains unclear.

Methods

This prospective cohort study was based on data from the National Health and Nutrition Examination Survey (1999–2018). Multinomial logistic regression was conducted to evaluate the relationship between MMA and CKM stages. Hazard ratios (HRs) and 96% CI for the association between MMA and mortality were calculated using the multivariable Cox regression model. The mediating role of MMA on the association between cardiometabolic biomarkers and mortality was estimating using mediation analysis.

Results

The cohort included 14 581 participants with CKM (median age, 55.0 [interquartile range, 41.0–69.0] years; 52.4% male). MMA showed a significant association with CKM stages (odds ratio, 3.73 [95% CI, 3.47–4.01]) after adjustment for multivariate covariates. During the median follow‐up of 102 months (interquartile range, 76–202), 3738 deaths occurred. Compared with participants in the lowest MMA quartile, those in the highest MMA quartile had HRs of 2.03 (95% CI, 1.78–2.30) for all‐cause death mortality, 2.31 (95% CI, 1.81–2.95) for cardiovascular mortality, and 1.92 (95% CI, 1.65–2.23) for noncardiovascular mortality, consistent with results in the CKM stage 1 to 2 and 3 to 4 groups. Furthermore, MMA mediated the association between several cardiometabolic biomarkers, such as physical activity, frailty, Life’s Simole 7 score, and estimated glomerular filtration rate levels, and mortality outcomes in the population with CKM.

Conclusions

Mitochondria‐derived MMA may serve as a valuable biomarker for evaluating CKM progression and mortality risk in individuals with CKM.

Keywords: biomarker, cardiovascular‐kidney‐metabolic, methylmalonic acid, mitochondria, mortality

Subject Categories: Cardiovascular Disease, Risk Factors

Nonstandard Abbreviations and Acronyms

CKM: cardiovascular‐kidney‐metabolic
MMA: methylmalonic acid
NHANES: National Health and Nutrition Examination Survey
PIR: poverty income ratio

Clinical Perspective.

What Is New?

Methylmalonic acid (MMA) is a value biomarker for evaluating cardiovascular‐kidney‐metabolic (CKM) syndrome stages.
Increased MMA is a risk factor of mortality among the population with CKM.
MMA serves as a mediator in the association of cardiometabolic biomarkers and mortality, such as physical activity, frailty, Life’s Simple 7 score, and estimated glomerular filtration rate levels.

What Are the Clinical Implications?

The population with early‐stage CKM should be closely monitored the MMA levels for the CKM management.
Future studies are needed to assess whether controlling MMA levels in individuals with CKM is beneficial in slowing progression and reducing mortality.

Cardiovascular‐kidney‐metabolic (CKM) syndrome, a concept recently articulated by the American Heart Association, is a complex multisystem disorder. ¹ Its central feature is the interaction between cardiovascular disease, renal insufficiency, and metabolic abnormalities (eg, diabetes, hyperlipidemia, and obesity), which mutually accelerate the progression of their respective pathological processes. ² With the growing global burden of chronic disease, CKM has become an increasingly important clinical challenge. ³ , ⁴ CKM typically progresses through multiple stages (0–4 stage), ranging from early metabolic disturbances to progressive impairment of renal and cardiac function, and may ultimately lead to end‐stage renal disease and severe cardiovascular events.

CKM is influenced by a variety of factors, including socioeconomic level, education, and comorbidities. ⁵ , ⁶ , ⁷ Therefore, identifying biomarkers closely related to the various stages of CKM can not only detect the disease at an early stage but also help to assess its progression and provide a basis for individualized management strategies.

Methylmalonic acid (MMA) is a byproduct of mitochondrial metabolism that incorporates into the tricarboxylic acid cycle under normal mitochondrial conditions to support energy production. ⁸ The level of MMA is affected by the activity of key mitochondrial enzymes, and the availability of cofactor vitamin B12. ⁹ Elevated MMA levels indicate the impaired mitochondrial metabolism. In addition, the accumulation of MMA may exacerbate oxidative stress by interfering with methylation reactions and metabolic enzyme activities in mitochondria, further damaging cells and tissues. ¹⁰ , ¹¹

In CKM, mitochondrial dysfunction has been recognized as a central mechanisms of its multisystem disease. ¹² , ¹³ , ¹⁴ Therefore, elevated MMA levels may serve as an early signal of CKM progression, reflecting declining mitochondrial function and worsening metabolic disorders. With advancing CKM, MMA may become a useful biomarker for predicting prognosis. At present, MMA is primarily used as a biomarker of vitamin B12 deficiency and inherited methylmalonic acidemia. ¹⁵ Furthermore, elevated circulating MMA has been identified as a marker of aging and a predictor of all‐cause mortality in the general population. ¹⁶ , ¹⁷ The association between MMA and CKM progression, along with its prognostic value in the population with CKM, has not been investigated.

This study aimed to evaluate the association of MMA with CKM staging and mortality risk in individuals with CKM syndrome. Additionally, we investigated the potential mediating role of MMA in the relationship between cardiometabolic biomarkers and mortality risk.

METHODS

Data for this study were obtained from the NHANES (National Health and Nutrition Examination Survey), administered by the US Centers for Disease Control and Prevention, and are publicly available. The data supporting the findings of this investigation, along with the materials and methodologies used in the analysis, are available upon reasonable request from the corresponding author.

Study Population and Design

NHANES is a biennial study executed by the US National Center for Health Statistics to evaluate the health and nutritional status of the American population. NHANES uses a stratified sampling technique to gather data through in‐person interviews, physical examinations, and laboratory tests, and is representative of the entire United States population. This study included 51 507 participants in 5 NHANES cycles (1999–2000, 2001–2002, 2003–2004, 2011–2012, and 2013–2014) for which MMA data were available. We excluded individuals lacking assessment of CKM grade variables (N=17 949), individuals without eligible MMA data (N=13 392), pregnancy (N=393), individuals lacking survival variables (N=19). Finally, 15 398 individuals were available for analysis (Figure S1).

Definition of CKM Syndrome

CKM was defined with reference to previous studies (Tables S1 through S3), which is characterized by the coexistence of clinical cardiovascular disease or metabolic disorders, chronic kidney disease (CKD), and subclinical cardiovascular disease. Clinical cardiovascular disease includes chronic heart failure, coronary artery disease, stroke, or heart attack, and subclinical cardiovascular disease is characterized by a 10‐year predicted cardiovascular disease risk of ≥20% or a significantly elevated risk of CKD.

According to the the Kidney Disease: Improving Global Outcomes methodology, CKD risk was assessed based on estimated glomerular filtration rate (eGFR) thresholds (≥60, 45–59, 30–44, and <30 mL/min per 1.73 m²) and urinary albumin to creatinine ratio (≥300, 30–299, and <30 mg/g). CKD was defined as moderate or high risk. ¹⁸

Metabolic disorders included overweight/obesity, abdominal obesity, prediabetes, diabetes, hypertension, dyslipidemia, and metabolic syndrome. A simplified version of the American Heart Association algorithm ¹⁹ was used to determine predicted 10‐year cardiovascular risk that takes into account sex, age, smoking, blood pressure, lipids, renal function, diabetes, antihypertensive medications, and statin use.

CKM was classified into 5 stages: stage 0 (normal condition), stage 1 (obesity or prediabetes), stage 2 (existence of CKD or metabolic risk factors), stage 3 (subclinical cardiovascular disease with metabolic disorders or CKD), and stage 4 (clinical cardiovascular disease with metabolic disorders or CKD). The subjects of this study were mainly the population with CKM (stage 1–4). To explore the relationship between MMA and CKM progression, participants with CKM stage 0 were included.

MMA Measurement

MMA was measured from plasma or serum samples. In the NHANES 1999 to 2004 data set, serum samples were fortified with an internal standard solution using a strong anion exchange resin. The isolated acid was derivatized with cyclohexanol to produce cyclohexyl esters, which were then separated by gas chromatography. Detection was performed using selective ion monitoring with a mass spectrometry detector, and quantification was achieved through internal calibration based on peak area ratios of MMA to the internal standard. In the NHANES 2011 to 2014 data set, MMA was chromatographically isolated from other substances, including isotopic succinic acid, which shares the same molecular mass as MMA, and quantified using liquid chromatography–tandem mass spectrometry. Quantification was executed by interpolating peak area ratios against a 6‐point aqueous calibration curve. The 2 measurements had excellent comparability and unbiased agreement. ²⁰ Moreover, serum and plasma MMA concentrations exhibited similar reference ranges and had comparable coefficients of variation. ²¹

Assessment of Covariates

Data from standardized questionnaires on age, race and ethnicity, sex, education attainment, physical activity, poverty income ratio (PIR), smoking status, and alcohol usage were included in this investigation. Mexican American, non‐Hispanic White, non‐Hispanic Black, Hispanic, and other (including Non‐Hispanic Asian, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, and multiracial individuals) were the classifications for race and ethnicity. Smoking status was identified as current smoker, former smoker, and never smoker. Ex‐smokers were identified as those who had consumed a minimum of 100 cigarettes but have since ceased smoking. Physical activity was categorized as vigorous, moderate, and light. Systolic blood pressure, body mass index, and waist circumference were measured during the physical examination. Laboratory parameters comprised lipids (total cholesterol, high‐density lipoprotein cholesterol), hemoglobin A1c (HbA1c), and eGFR. Definitions and scores for Life’s Simple 7 and frailty are detailed in Tables S4 and S5.

Death Ascertainment

Dates of death and causes of death up to December 31, 2019 from the National Death Index records were collected. The primary causes of mortality were identified with International Classification of Diseases, Tenth Revision (ICD‐10) codes. The mortality outcomes were all‐cause, cardiovascular, and noncardiovascular deaths. Cardiovascular deaths were classified as ICD‐10 codes, and the diseases included are listed in the Table S6.

Statistical Analysis

Covariates with missing values (<5.0% for each variable, detailed in Table S7) were imputed under the assumption of missing at random using multiple imputation by chained equations with 10 imputations implemented in the mice package in R. The imputed variables included educational attainment, smoking status, PIR, physical activity, and Life’s Simple 7. Continuous variables were expressed as mean±SD or median with interquartile range, with comparisons performed using t tests or Kruskal–Wallis tests according to normal and nonparametric distributions, respectively. MMA is a nonnormal distribution, which we log‐transformed. Pearson’s chi‐square test was used to compare categorical variables, which were represented as counts with percentages. Partial correlation study was conducted to evaluate the link between log‐transformed MMA and variable cardiometabolic biomarkers, with adjustment of age, race and ethnicity, sex, and smoking status, and reported the correlation values (r). We employed multinomial logistic regression using the ‘nnet’ package in R software to evaluate the association between MMA and CKM stages ranging from 0 to 4, while adjusting for multivariable (age, sex, race and ethnicity, educational attainment, alcohol intake, smoking status, PIR, and physical activity). Odds ratio (OR) and 95% (CIs) were reported. We conducted the restricted cubic spline with multivariable‐adjusted Cox proportional hazards models to visualize the association between log‐transformed MMA and mortality. In parallel, we also generated restricted cubic spline curves for CRP (C‐reactive protein) and the triglyceride‐glucose index in relation to mortality, to facilitate comparison with the association observed for MMA. MMA values were divided into 4 groups by quartile: Q1 (<106.0 nmol/L), Q2 (106.0–140.0 nmol/L), Q3 (140.0–190.0 nmol/L), and Q4 (>190.0 nmol/L). The association of MMA with mortality was evaluated using the crude and multivariate Cox proportional hazards models for the entire population with CKM (stages 1–4), combined groups of stages 1–2 and stages 3–4, with hazard ratios (HRs) and 95% CI were reported. The cumulative rate of all‐cause, cardiovascular, and noncardiovascular death was estimated using Kaplan–Meier curves, with intergroup differences evaluated through the log‐rank test.

To validate the results, we conducted 2 sensitivity analyses, the first excluded patients with cancer, and the second omitted those who died within 2 years of follow‐up. In addition, we incorporated NHANES sampling weights, strata, and primary sampling units into the regression models to account for the complex survey design. We further performed stratified analyses of the correlation between MMA and outcome of mortality, grouped by age (<65 and ≥65 year), sex (male and female), body mass index (<30 and ≥30 kg/m²), and cycle (NHANES 1999–2004 and NHANES 2011–2014). Subgroup interactions were evaluated using likelihood ratio tests to assess the relationship of MMA on mortality across different subgroups.

Finally, the use of MMA as a mediator could help to elucidate how cardiometabolic biomarkers affect death through MMA. We used the ‘mediator’ package in the R software to analyses the potential mediating role of MMA between various exposures and mortality outcomes and constructed adjusted models to estimate the direct and indirect mediating effects of MMA.

All statistical analyses were conducted using R software, version 4.4.0 (R Project for Statistical Computing, Vienna, Austria). A P value of <0.05 was considered statistically significant.

Ethics Statement

The NHANES is conducted by the National Center for Health Statistics and was approved by the Ethics Review Board of National Center for Health Statistics to ensure the protection of all human participants, and all participants provided written informed consent (http://www.cdc.gov/nchs/nhanes/irba98.htm).

RESULTS

Baseline Characteristics

The baseline characteristics of the study participants are shown in Table 1, arranged by MMA quartiles. Detailed baseline information based on CKM‐stage group is described in Table S8. Among the 14 581 participants with CKM (stage 1–4), the mean±SD age was 54.5 (17.5) years, and 7639 (52.4%) were male. Most of the participants were CKM stage 2. Participants with higher MMA levels were generally older, more likely to be White, and had lower levels of physical activity. During a median follow‐up of 102 months (interquartile range, 76–102), 3738 deaths occurred, including 1064 cardiovascular deaths (0.66 per 100 patient‐years) and 2674 noncardiovascular deaths (1.66 per 100 patient‐years).

Table 1.

Baseline Characteristic of CKM (Stage 1–4) Participants According to Methylmalonic Acid

Characteristic	Total N=14 581	Q1 N=3700	Q2 N=3877	Q3 N=3409	Q4 N=3595	P value
Age, y	55.0 (41.0, 69.0)	45.0 (34.0, 59.0)	53.0 (40.0, 65.0)	59.0 (44.0, 70.0)	67.0 (52.5, 78.0)	<0.001
Sex, n (%)						<0.001
Male	7639 (52.4)	1813 (49.0)	2091 (53.9)	1824 (53.5)	1911 (53.2)
Female	6942 (47.6)	1877 (51.0)	1786 (46.1)	1585 (46.5)	1684 (46.8)
Race and ethnicity, n (%)						<0.001
Non‐Hispanic White	6956 (47.7)	1071 (28.9)	1799 (46.4)	1872 (54.9)	2214 (61.6)
Non‐Hispanic Black	3070 (21.1)	1082 (29.2)	868 (22.4)	629 (18.5)	491 (13.7)
Mexican American	2469 (16.9)	925 (25.0)	671 (17.3)	455 (13.3)	418 (11.6)
Hispanic and other	2086 (14.3)	622 (16.8)	539 (13.9)	453 (13.3)	418 (11.6)
Education, n (%)						<0.001
College or above	6874 (47.1)	1774 (47.9)	1901 (49.0)	1688 (49.5)	1511 (42.0)
High school or equivalent	5672 (38.9)	1424 (38.5)	1518 (39.2)	1284 (37.7)	1446 (40.2)
Less than high school	2035 (13.9)	502 (13.6)	455 (11.9)	437 (12.8)	628 (17.8)
Poverty income ratio, n (%)						<0.001
≤1	2960 (20.3)	816 (22.1)	731 (18.9)	654 (192)	759 (21.1)
1–3	6177 (42.4)	1519 (41.1)	1557 (40.2)	1391 (40.8)	1710 (47.6)
≥3	5444 (37.3)	1365 (36.9)	1589 (41.0)	1364 (40.0)	1126 (31.3)
Alcohol use, n (%)						<0.001
Nondrinker	10 021 (68.7)	2270 (61.4)	2584 (66.6)	2384 (69.9)	2783 (77.4)
Mild drinker	571 (3.9)	193 (4.7)	149 (3.8)	149 (4.4)	100 (2.8)
Moderate drinker	2100 (14.4)	618 (16.7)	602 (15.5)	506 (14.8)	374 (10.4)
Heavy drinker	1889 (13.0)	639 (17.3)	542 (14.0)	370 (10.9)	338 (9.4)
Smoking, n (%)						<0.001
Never smoker	7393 (50.7)	2017 (54.5)	1981 (51.1)	1694 (49.7)	1701 (47.3)
Former smoker	4208 (28.9)	852 (23.0)	1097 (28.3)	1027 (30.1)	1232 (34.3)
Current smoker	2980 (20.4)	831 (22.5)	799 (20.6)	688 (20.2)	662 (18.4)
Physical activity, n (%)						<0.001
Less than moderate	8962 (61.5)	2187 (59.1)	2311 (59.6)	2056 (60.3)	2408 (67.0)
Moderate	3614 (24.8)	920 (24.9)	963 (24.9)	873 (25.6)	858 (23.9)
Vigorous	2000 (13.7)	592 (16.0)	601 (15.5)	479 (14.1)	328 (9.1)
Laboratory indicators
Vitamin B12, pmol/L	360.1 (269.4, 490.8)	404.0 (314.0, 538.0)	379.0 (286.0, 503.0)	352.0 (269.0, 484.0)	298.0 (216.0, 418.0)	<0.001
Hemoglobin A1c, %	5.5 (5.2, 5.9)	5.5 (5.2, 5.9)	5.5 (5.2, 5.9)	5.5 (5.2, 5.9)	5.6 (5.3, 6.1)	<0.001
Total cholesterol, mg/dL	198.0 (171.0, 226.0)	198.0 (171.0, 224.0)	200.0 (174.0, 226.0)	198.0 (172.0, 226.0)	195.0 (167.0, 225.0)	<0.001
High‐density lipoprotein cholesterol, mg/dL	49.0 (41.0, 60.0)	49.0 (41.0, 60.0)	49.0 (41.0, 60.0)	49.0 (41.0, 60.0)	48.0 (40.0, 59.0)	<0.001
Estimated glomerular filtration rate, mL/min per 1.73 m²	89.7 (72.3, 105.0)	103.0 (89.8, 116.0)	92.7 (78.7, 105.0)	85.1 (69.6, 99.2)	71.8 (52.9, 90.5)	<0.001
Life Simple’s 7 score	7.0 (6.0, 9.0)	8.0 (6.0, 9.0)	7.0 (6.0, 9.0)	7.0 (6.0, 9.0)	7.0 (5.0, 8.0)	<0.001
Frailty score *10	1.4 (0.8, 2.2)	1.3 (0.8, 1.8)	1.3 (0.8, 2.0)	1.4 (0.9, 2.2)	1.8 (1.1, 2.8)	<0.001
10‐y cardiovascular disease risk score	6.0 (1.6, 16.3)	2.4 (0.8, 7.5)	4.8 (1.4, 12.5)	7.4 (2.2, 17.2)	15.6 (4.9, 27.4)	<0.001
CKM stage, n (%)						<0.001
1	1455 (10.0)	572 (15.5)	415 (10.7)	300 (8.8)	168 (4.7)
2	9160 (62.8)	2726 (73.7)	2705 (69.8)	2139 (62.7)	1590 (44.2)
3	1843 (12.6)	136 (3.7)	341 (8.8)	475 (13.9)	891 (24.8)
4	2123 (14.6)	266 (7.2)	416 (10.7)	495 (14.5)	946 (26.3)

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CKM indicates cardiovascular‐kidney‐metabolic syndrome.

Mortality rates increased substantially with advancing CKM stage, and individuals with elevated MMA levels exhibited a heightened mortality risk compared with those with lower MMA level (Figure S2).

MMA and Cardiometabolic Biomarkers Correlation

Table S9 presents partial correlation analyses between MMA levels and several cardiometabolic indicators after adjustment for age, sex, race, and ethnicity. In the overall population with CKM, eGFR, Life’s Simple 7 score, and body mass index exhibited a strong negative connection with MMA, with correlation values of −0.164, −0.104, and −0.047, respectively (all P<0.001). Conversely, frailty score (r=0.209, P<0.001), HbA1c (r=0.036, P<0.001), and alcohol consumption (r=0.016, P=0.047) showed significantly positive correlations with MMA.

Similar correlations patterns between these cardiometabolic biomarkers and MMA were observed in participants with CKM stage 1 to 2 and stage 3 to 4. In addition, physical activity and PIR were negatively correlated with MMA in the overall population with CKM and in participants with CKM stage 3 to 4.

Association Between MMA and CKM Stages

The relationships between MMA and CKM stages from 0 to 4 are shown in Figure 1. Compared with participants in the lowest MMA quantile (Q1), those in higher quantiles were linked to an increased likelihood of progressing from CKM stage 0 to more advanced CKM stages. For instance, in Q4, the ORs for progressing from CKM stage 0 to stage 2, 3, and 4, were 1.58 (95% CI, 1.25–2.01), 2.27 (95% CI, 1.63–3.18), and 2.25 (95% CI, 1.67, 3.02), respectively. Similar trends were observed when CKM stage 1 and stage 2 serve as reference groups. The association across the MMA quantiles was not statistically significant when CKM stage 3 used as the reference.

Multinomial logistic regression models were adjusted for age, sex, race and ethnicity, smoking status, alcohol consumption, physical activity, education level, and poverty income ratio. CKM indicates cardiovascular‐kidney‐metabolic syndrome; MMA, methylmalonic acid; and OR, odds ratio.

Association of MMA and Mortality in Participants With CKM

Throughout the whole population with CKM, higher MMA levels were strongly associated with all‐cause, cardiovascular, and noncardiovascular deaths in the multivariable Cox proportional hazards models (Table 2 and Table S10). Compared with the lowest quartile of MMA, those in the highest MMA quartile exhibited an elevated risk of all‐cause mortality (HR, 2.03 [95% CI, 1.78–2.30]), cardiovascular mortality (HR, 2.31 [95% CI, 1.81–2.95]), and noncardiovascular mortality (HR, 1.92 [95% CI, 1.65–2.23]). Similar associations were observed in both the CKM stage 1 to 2 and stage 3 to 4 subgroups (Tables S11 and S12).

Table 2.

HRs (95% CI) for the Risk of Death in Participants With CKM (Stage 1–4) With Different Level of MMA

Event	Crude HR (95% CI)	P value	Adjusted HR (95% CI)	P value
All‐cause death
Q1	Reference		Reference
Q2	1.69 (1.51–1.90)	<0.001	1.04 (0.93–1.16)	0.523
Q3	2.67 (2.39–2.99)	<0.001	1.27 (1.13–1.43)	<0.001
Q4	6.07 (5.38–6.87)	<0.001	2.03 (1.78–2.30)	<0.001
Cardiovascular death
Q1	Reference		Reference
Q2	1.62 (1.29–2.04)	<0.001	0.96 (0.76–1.20)	0.705
Q3	2.96 (2.37–3.69)	<0.001	1.32 (1.05–1.65)	0.016
Q4	7.66 (6.07–9.67)	<0.001	2.31 (1.81–2.95)	<0.001
Noncardiovascular death
Q1	Reference		Reference
Q2	1.72 (1.51–1.96)	<0.001	1.07 (0.93–1.22)	0.333
Q3	2.58 (2.26–2.94)	<0.001	1.26 (1.09–1.44)	0.001
Q4	5.53 (4.79–6.38)	<0.001	1.92 (1.65–2.23)	<0.001

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Adjusted for age, sex, race and ethnicity, smoking status, alcohol consumption, physical activity, education level, and poverty income ratio. CKM indicates cardiovascular‐kidney‐metabolic; HR, hazard ratio; and MMA, methylmalonic acid.

Elevated MMA levels were also associated with increased 3‐year and 5‐year mortality risk (Table S13). To address potential bias from cancer and short‐term mortality, we conducted sensitivity analyses excluding participants with cancer (Tables S14 and S15) and those who died within the 2 years of follow‐up (Tables S16 and S17). The results of these analyses were consistent with the main findings, reinforcing the robustness of the association between MMA and mortality outcomes in individuals with CKM.

The restricted cubic spline analysis further demonstrated significant association of MMA with all‐cause, cardiovascular, and noncardiovascular deaths in the population with CKM, with consistent results at both CKM stage 1 to 2 and CKM stage 3 to 4 (all P _‐overall<0.05 and P _{‐interaction}<0.001) (Figure 2). In addition, the elevated CRP and triglyceride‐glucose index were also the risk factors of mortality in participants with CKM (Figures S3 and S4). The Kaplan–Meier curves indicate that, even after 15 years of follow‐up, participants in the highest MMA quartile had a heightened mortality risk compared with those in the lowest quartile, and this pattern was consistent across CKM stages (Figure 3).

All‐cause death (A), cardiovascular death (B), noncardiovascular death (C) for all participants with CKM (stage 1–4). All cause death (D), cardiovascular death (E), noncardiovascular death (F) for participants with CKM stage 1 to 2 group. All‐cause death (G), cardiovascular death (H), noncardiovascular death (I) for participants with CKM stage 3 to 4 group. Log‐rank P values from log‐rank test. CKM indicates cardiovascular‐kidney‐metabolic syndrome; and MMA, methylmalonic acid.

No significant interactions with mortality were observed in the adjusted Cox regression model across age, sex, body mass index, and cycle subgroups. In the vitamin B12 subgroup, elevated MMA correlated with a heightened risk of mortality, particularly among participants with vitamin B12 ≥400 pmol/L group, but there was an interaction effect for all‐cause death and cardiovascular death (P _{‐for‐interaction}=0.006, P _{‐for‐interaction}=0.005, respectively) (Table S18).

Mediating Role of MMA in Cardiometabolic Biomarkers and Mortality Risk

Our mediation analyses across the population with CKM suggested that MMA serves as an important mediator in the complex network of relationships between multiple cardiometabolic biomarkers and mortality (Tables S19 through S21).

For all‐cause death, MMA showed its mediating role through several cardiometabolic biomarkers, including physical activity, frailty score, Life’s Simple 7 score, eGFR, HbA1c, and PIR. The largest mediation effects were observed for physical activity (mediated proportion, 17.4% [95% CI, 13.7%–22.1%]), frailty score (16.5% [95% CI, 14.8%–18.8%]), Life’s Simple 7 score (12.3% [95% CI, 10.6%–14.2%]), and eGFR (9.9% [95% CI, 7.8%–12.1%]). In addition, MMA mediated the associations of frailty score, physical activity, Life’s Simple 7 score, and eGFR with cardiovascular death by 15.1%, 13.2%, 10.6%, and 8.9%, respectively. For noncardiovascular death, MMA mediated the associations with physical activity (18.6%), frailty score (17.9%), and HbA1c (10.4%), and PIR (3.4%) (Figure 4).

CKM indicates cardiovascular‐kidney‐metabolic syndrome; eGFR, estimated glomerular filtration rate; and MMA, methylmalonic acid.

DISCUSSION

This extensive prospective study involving 14 581 adults from a nationally representative sample in the United States established a correlation between MMA and the progression of CKM. Elevated MMA levels were significantly associated with an increased risk of all‐cause, cardiovascular, and noncardiovascular mortality in patients with CKM, with comparable findings observed in patients with CKM stages 1 to 2 and stages 3 to 4. Mediation analyses revealed that MMA played a key mediating role in the association of physical activity, frailty, Life’s Simple 7 score, and eGFR with mortality. We show for the first time with a large prospective cohort study that baseline MMA levels were significantly associated with long‐term all‐cause and cardiovascular mortality risk in a population with CKM. Notably, the correlation between MMA levels and death remained consistent regardless of vitamin B12 levels.

Previous studies have identified MMA as an independent predictor of mortality risk. One prior NHANES study conducted in the general population reported that elevated MMA levels were significantly linked to all‐cause and cardiovascular mortality. ¹⁶ In older adults (aged 60–75 years), MMA independently predicted the increased mortality. ²² A separate investigation with 1755 patients with coronary heart disease revealed that individuals in the highest MMA quartile exhibited markedly elevated all‐cause mortality (HR, 1.70 [95% CI, 1.31–2.20]) and cardiovascular mortality (HR, 2.00 [95% CI, 1.39–2.89]) relative to those in the lowest quartile. ²³ In addition, 2 prospective cohort analyses from Europe showed that elevated MMA levels were correlated with a heightened risk of acute myocardial infarction and mortality in patients with coronary artery disease and that associations were stronger in older adults and in smokers. ²⁴ A metabolomics study comparing serum metabolic profiles between older (age ≥60 years) and younger (age ≤30 years) adults found that MMA concentrations were consistently higher in the older age group. ²⁵ Aging is also characterized by chronic inflammation, commonly referred to as “inflammaging.” ²⁶ Vitamin B12 depletion caused by itoic acid may represent a potential mechanism underlying age‐related increases in circulating MMA. ²⁷ Also, MMA has a dual effect on tumor cells, with exogenous MMA in the circulation and endogenous MMA produced by tumor cells inducing the expression of SOX4 (sex‐determining region Y‐box 4) and IL‐6 (interleukin‐6) or the downregulation of methyl‐malonyl coenzyme A epimerase, which promotes tumor cell growth and invasion, predicting a poor prognosis. ²⁸ , ²⁹ Although MMA has recently gained attention as a biomarker of aging and cancer, our findings extend this perspective by suggesting a novel and potentially important association between MMA levels and the progression of CKM syndrome.

In the sensitivity analyses, the relationship between elevated MMA and heightened mortality risk persisted significant after excluding participants with the cancer. To minimize the influence of age and severe comorbidities (eg, cancer, advanced heart failure, renal failure, etc.) on short‐term life expectancy, we further excluded individuals who died within the first 2 years of follow‐up. These exclusions did not materially alter the associations of MMA with mortality in the population with CKM.

In a US cohort from 2011 to 2020 comprising 10 762 subjects, approximately 90% met the diagnostic criteria for CKM syndrome (stage 1 or higher), and 15% had advanced stages. ³⁰ Older adults and men accounted for a greater proportion of the advanced stages of CKM cases. Individuals with preexisting cardiovascular disease, chronic kidney disease, metabolic risk factors, or a combination of these conditions had a higher risk of adverse prognosis compared with those with a singular risk factor. ³¹ Early screening and prevention of CKM progression have become critical clinical and public health issues. Biomarkers currently used for early screening of CKM include lipids, glucose, renal function, BNP (B‐type natriuretic peptide), and troponin. ¹

However, few studies have determined the relationship between conventional biomarkers and long‐term prognosis in the population with CKM. In contrast, our study demonstrated that MMA levels were associated with CKM progression and increased risk of all‐cause and cardiovascular death. CKM is characterized by coexistence of cardiovascular disease, chronic kidney disease, and metabolic risk factors, with systemic inflammation and impaired mitochondrial metabolism as common pathological features. ³² , ³³ , ³⁴ , ³⁵ Therefore, MMA may serve as a potential early screening index for CKM and a predictor of long‐term mortality risk.

MMA is most commonly regarded as a marker of vitamin B12 deficiency. However, vitamin B12 deficiency accounts for only part of the elevated MMA in adults. ³⁶ , ³⁷ Mitochondrial dysfunction is considered a major contributor to elevated MMA levels. ³⁸ , ³⁹ In our study, the association between MMA and CKM progression persisted across different vitamin B12 strata, supporting the hypothesis that mitochondrial impairment may play a central role. Reduced mitochondrial respiration can disrupt lysosomal hydrolysis, resulting in the accumulation of iron and cobalamin within lysosomes, serving as a pathological foundation for the development of heart failure. ⁴⁰ Plasma MMA levels have been reported to be significantly higher in patients with heart failure than in healthy controls and to correlate with the presence of comorbidities. ⁴¹ In the present study, MMA also plays a crucial mediating role in the association of frailty, physical activity, PIR, and eGFR with long‐term mortality in the population with CKM. Of note, MMA has been linked to various lifestyle and health‐related factors, including alcohol consumption, HbA1c, and Life’s Simple 7. These findings suggest that MMA may represent an integrative marker, capturing the influence of both health‐related behaviors and metabolic pathways on mortality risk.

Strengths and Limitations

This study has certain strengths. We used a large, nationally representative sample to investigate the association between MMA and long‐term all‐cause and cardiovascular deaths in the population with CKM. Our findings further suggest that MMA is associated with the progression of CKM, providing new insights into risk stratification and management.

There are also several limitations of this study. First, this study did not evaluate the association between longitudinal changes in MMA levels and clinical outcomes, which may provide more information than a single baseline measurement. Moreover, the absence of CKM data in some participants should also acknowledged as a limitation. Second, different methods of measuring and analyzing MMA were used at different cycles of NHANES. Although numerical adjustments were used to harmonize these data, some inconsistencies remain compared with measurements obtained at the same time and in the same laboratory. Third, the observational design of this study limits the ability to fully exclude residual confounding and to establish causal relationship between MMA, CKM stage, and mortality. Finally, due to the lack of direct mitochondrial biomarkers in the data set, we could not directly assess the relationship between mitochondrial dysfunction and CKM progression. Future studies with longitudinal measurements of MMA and risk factors, or causal inference approaches, are warranted to further validate these findings.

CONCLUSIONS

Mitochondria‐derived MMA may serve as a valuable biomarker for evaluating CKM stages and mortality risk and may also act as a mediator linking several cardiometabolic factors, such as physical activity, frailty, Life’s Simple 7 score, and eGFR levels, to mortality outcomes in the population with CKM.

Sources of Funding

National Key Research and Development program of China (2022YFC3601304), Jiangxi key technology R&D program (2023BBG71011).

Disclosures

Hongyu Liu is a visiting research fellow in the Liverpool Center for Cardiovascular Science at University of Liverpool and Funded by the China Scholarship Council (file No. 202406820109), which is a nonprofit institution affiliated with the Ministry of education of the P.R. China. All the other authors declare no competing interests related to this article.

Supporting information

Tables S1–S21

Figures S1–S4

JAH3-15-e043066-s001.pdf^{(989.8KB, pdf)}

Acknowledgments

We appreciate the dedication of the entire NHANES staff and all the participants in the NHANES program. We extend our gratitude to Dr Jing Zhang of the Second Department of Infectious Disease at Shanghai Fifth People’s Hospital, Fudan University, for his contributions to the development of the NHANES R package and website, which offer the most efficient means for analyzing NHANES data. We confirmed that we had the permission of Dr Jing Zhan, to be acknowledged in this article. Hongyu Liu, Yang Chen, and Kui Hong were responsible for the conception and design of the study. Hongyu Liu, Shuang Wu, and Yang Chen performed data analysis. Bi Huang, Yang Liu, Ziyi Zhong, Manlin Zhao, Yimeng Wang, Tommaso Bucci, and Chunyu Liu reviewed the articlefor intellectual contribution. Gregory Y. H. Lip participated in the review and edition of the article. All authors participated in the interpretation of the results and confirmed the final version of the article.

This article was sent to Sakima Ahmad Smith, MD, MPH, Associate Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.125.043066

For Sources of Funding and Disclosures, see page 12.

Contributor Information

Yang Chen, Email: yang.chen2@liverpool.ac.uk.

Kui Hong, Email: hongkui88@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Tables S1–S21

Figures S1–S4

JAH3-15-e043066-s001.pdf^{(989.8KB, pdf)}

[jah311562-bib-0001] 1. Ndumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, Coresh J, Baker‐Smith CM, Carnethon MR, Després J‐P, et al. A synopsis of the evidence for the science and clinical management of cardiovascular‐kidney‐metabolic (CKM) syndrome: a scientific statement from the American Heart Association. Circulation. 2023;148:1636–1664. doi: 10.1161/CIR.0000000000001186 [DOI] [PubMed] [Google Scholar]

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[jah311562-bib-0004] 4. Zhu R, Wang R, He J, Wang L, Chen H, Niu X, Sun Y, Guan Y, Gong Y, Zhang L, et al. Prevalence of cardiovascular‐kidney‐metabolic syndrome stages by social determinants of health. JAMA Netw Open. 2024;7:e2445309. doi: 10.1001/jamanetworkopen.2024.45309 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Effect of Mitochondria‐Derived Methylmalonic Acid on Progress and Mortality Risk in US Adults With Cardiovascular‐Kidney‐Metabolic Syndrome

Hongyu Liu, MD

Shuang Wu, PhD

Bi Huang, PhD

Yang Liu, MD

Ziyi Zhong, MRes

Manlin Zhao, MD

Yimeng Wang, MD

Tommaso Bucci, PhD

Chunyu Liu, MBBS

Yang Chen, MD

Kui Hong, PhD

Gregory YH Lip, MD

Abstract

Background

Methods

Results

Conclusions

Nonstandard Abbreviations and Acronyms

Clinical Perspective.

What Is New?

What Are the Clinical Implications?

METHODS

Study Population and Design

Definition of CKM Syndrome

MMA Measurement

Assessment of Covariates

Death Ascertainment

Statistical Analysis

Ethics Statement

RESULTS

Baseline Characteristics

Table 1.

MMA and Cardiometabolic Biomarkers Correlation

Association Between MMA and CKM Stages

Figure 1. Association between MMA quartiles and CKM stages (0–4).

Association of MMA and Mortality in Participants With CKM

Table 2.

Figure 2. Kaplan–Meier survival curves for mortality outcomes across MMA quartiles in participants with CKM (stage 1–4).

Figure 3. Restricted cubic spline analyses for the relationship between log‐transformed MMA and mortality outcomes among participants with CKM (stage 1–4).

Mediating Role of MMA in Cardiometabolic Biomarkers and Mortality Risk

Figure 4. Mediation effects of MMA on the relationships between exposures with all cause death (A), cardiovascular death (B), and noncardiovascular death (C) in the entire population with CKM.

DISCUSSION

Strengths and Limitations

CONCLUSIONS

Sources of Funding

Disclosures

Supporting information

Acknowledgments

Contributor Information

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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