
==== Front
Diabetol Metab Syndr
Diabetol Metab Syndr
Diabetology & Metabolic Syndrome
1758-5996
BioMed Central London

1461
10.1186/s13098-024-01461-2
Research
Comparison of the correlation of creatinine- and cystatin C–Based estimated GFR and their differences with new-onset heart failure in a community-based population with type 2 diabetes
Sang Dasen drsang@126.com

1
Tao Jie 1
Song Wanqing 1
Zhang Qi 2
Wu Shouling 3
Geng Wei docgwei@163.com

1
1 https://ror.org/022nvaw58 0000 0005 0178 2136 Department of Cardiology, Baoding NO. 1 Central Hospital, N0. 320, Changcheng Street, Baoding, Hebei China
2 https://ror.org/00sr40296 grid.440237.6 0000 0004 1757 7113 Department of Cardiology, Tangshan Gongren Hospital Affiliated to Hebei Medical University, Tangshan, Hebei 063000 China
3 https://ror.org/01kwdp645 grid.459652.9 0000 0004 1757 7033 Department of Cardiology, Kailuan General Hospital, 57 Xinhua Road(East), Tangshan, Hebei China
12 9 2024
12 9 2024
2024
16 22521 7 2024
30 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Aims

This study aimed to investigate the impact of different estimated glomerular filtration rate (eGFR) values like cystatin C-based eGFR (eGFRcys), creatinine-based eGFR (eGFRcr), and their difference (eGFRdiff; eGFRcys -eGFRcr), on the incidence of heart failure (HF) in patients with type 2 diabetes(T2D).

Methods

Being a prospective cohort study, it included 7,967 patients with T2D who underwent serum creatinine and cystatin C tests as part of the Kailuan Group’s 6th annual health examination (2016). Subsequently, eGFRcys, eGFRcr, and eGFRdiff were calculated. Patients were categorized into three groups: negative (<-15 mL/min/1.73 m2), midrange (-15 to 15 mL/min/1.73 m2), and positive (> 15 mL/min/1.73 m2) eGFRdiff groups, respectively. Furthermore, the relationship between the various eGFR measurements and new-onset HF were studied using Cox proportional hazards regression, and the potential improvement in predictive capability was evaluated by adding these eGFR metrics to established HF risk models.

Results

Among 7967 participants with mean age of 60.51 years, there were 20.92% women and 79.08% men. At baseline, eGFRcys and eGFRcr values differed by more than 15 mL/min/1.73m2 in 41.3% of participants. During a median follow-up period of 3.76 years, there were 172 (2.16%) new HF cases and 517 (6.49%) all-cause deaths. The cumulative incidence of HF in the midrange, negative, and positive eGFRdiff groups was 1.74%, 4.10%, and 0.61%, respectively (p < 0.001). In multivariable adjusted models, participants in the negative eGFRdiff group had higher risk of HF compared with the midrange eGFRdiff group (HR, 2.15; 95% CI, 1.57–2.94). Conversely, participants in the positive eGFRdiff group had lower risk for HF (HR, 0.40; 95% CI, 0.17–0.93). And each 15 mL/min/ 1.73 m2 higher eGFRdiff was associated with 34% (HR, 0.66; 95% CI, 0.58 − 0.47)lower risk of incident HF. The predictive capacity for HF risk in diabetic individuals was enhanced by adding eGFRcys or eGFRdiff to established HF risk models, with eGFRcys showing more significant additional predictive value.

Conclusion

These findings suggest that large differences between eGFRcys and eGFRcr were common in community-based population with T2D. Different eGFR metrics can independently predict HF incidence in patients with T2D. Additionally, metrics like eGFRcys and eGFRdiff provide significant predictive value for HF risks beyond traditional risk factors, with eGFRcys showing more pronounced benefits in such cases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13098-024-01461-2.

Keywords

Stimated glomerular filtration rate
Heart failure
Type 2 diabetes
Serum creatinine
Cystatin C
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcIntroduction

Heart failure (HF) is the terminal stage of several cardiovascular diseases. The incidence of HF is rising annually due to an aging population and the ongoing prevalence of metabolic risk factors like hypertension and diabetes [1]. In developed countries, the adult HF prevalence is approximately 1–2%, which can escalate up to 10% in those > 70 years [2, 3]. In China, the adult HF prevalence increased from 0.9 to 1.3% in 2003 and 2012–2015, with approximately 5 million additional cases during this period [4, 5].Despite significant advances in HF treatment, its prognosis remains poor, with an approximately 50% 5-year mortality rate [6]. This underscores the importance of accurately identifying the high-risk populations for providing early targeted interventions to effectively prevent HF onset.

The glomerular filtration rate estimated by serum creatinine (eGFRcr) is the most accurate indicator for assessing renal function. Moreover, a decline in eGFRcr is an independent risk factor for predicting the onset, progression, and poor prognosis of HF [7–9]. Compared to serum creatinine, cystatin C remains unaffected by muscle mass, age, and gender [10, 11]. Therefore, a stronger and more linear association has been observed between eGFRcys decline and the risks of HF, cardiovascular disease, and mortality [12, 13]. However, discrepancies between eGFRcr and eGFRcys are common. The difference between eGFRcys and eGFRcr (eGFRdiff), a novel variable, has significant predictive value for incident HF in the general population and chronic kidney disease patients (CKD) [14, 15].

However, existing research has mainly focused on the general population and CKD individuals. The predictive value of different eGFR metrics based on cystatin C and serum creatinine for HF incidence in diabetic patients at high risk for HF and CKD has not yet been assessed. Therefore, we investigated the predictive value of different eGFR metrics for HF incidence in the population with type 2 diabetes(T2D) by using the Kailuan study data.

Materials and methods

Study cohort

Being a prospective cohort study, health examinations for active and retired Kailuan Group personnel were conducted every two years from June 2006 to October 2007 in 11 hospitals, including Kailuan General Hospital and its affiliated hospitals. Follow-up assessments included incident HF and mortality. In the 6th health examination in 2016, cystatin C was investigated in T2D patients. We selected T2D patients who participated in this health examination and underwent cystatin C investigations as our study subjects. The inclusion criteria were: (1) Those who participated in the 2016 annual health examination and met the diagnostic criteria for T2D; (2) Patients with the availability of primary research data, including cystatin C and serum creatinine, and (3) Those willing to participate and provide informed consent. The exclusion criteria were: (1) Patients with a history of HF before the health examination and (2) Those having valvular and congenital heart diseases, respectively.

Collection of general clinical data and laboratory investigations

Patient data like age, gender, personal history, disease history, and medication usage were obtained through face-to-face interviews. We measured height, weight, blood pressure, heart rate, and relevant biochemical indicators by following previously published methods [16]. Smoking was defined as averaging at least one cigarette per day > 1 year or having quit smoking < 1 year ago. Additionally, body mass index (BMI) was calculated as weight / height2 (kg/m2).

Calculation and grouping of eGFRcr and eGFRcys

We used the 2012 CKD Epidemiology Collaboration (CKD-EPI) cystatin C equation and 2021 race-free CKD-EPI equations to calculate eGFRcys and eGFRcr, respectively [10, 17], and eGFRdiff = eGFRcys- eGFRcr.

The subjects were divided into 3 groups according to eGFRdiff level: negative eGFRdiff group: lower than − 15 mL/min/1.73 m2, with eGFRcys lower than eGFRcr; midrange eGFRdiff group: -15 to 15 mL/min/1.73 m2, with eGFRcys similar to eGFRcr; positive eGFRdiff group: 15 mL/min/1.73 m2 or greater, with eGFRcys higher than eGFRcr.

Diagnostic criteria.

T2D: The American Diabetes Association (ADA) Criteria for Diagnosis of Diabetes (2010) was referred [18].

1) History of T2D;

Or 2) Fasting blood glucose (FBG) ≥ 7.0 mmol/L;

Or 3) Two-hour blood glucose of ≥ 11.1 mmol/L in random plasma glucose test or oral glucose tolerance test;

Or 4) Hemoglobin A1c (HbA1c) ≥ 6.5%(47.5 mmol/mol).

HF: Chinese Guidelines for the Diagnosis and Treatment of Chronic Heart Failure (2018) was referred [19].

Symptoms and signs of HF, manifested as shortness of breath, fatigue, palpitations, fluid retention, as well as New York Heart Association (NYHA) heart function grade II and above;

Modified Simpson’s method: the left ventricular ejection fraction < 50% measured by echocardiography;

Plasma N-terminal pro-B-type natriuretic peptide ≥ 125 ng/L.

The diagnosis must meet conditions (1) as well as at least one of conditions (2) and (3).

Follow-up and endpoint events

After the completion of the 6th health examination, that is, the starting point of follow-up, trained medical staff reviewed the inpatient diagnosis and recorded the end-point events of the participants in the Affiliated Hospitals of Kailuan Group and the Designated Hospitals for Medical and Health Insurance of China every year. The end-point events ware defined as HF during the follow-up. The time of the first event was considered as the end-point for those with > 2 events, and the final follow-up date for those without HF was December 31, 2020. All diagnoses were confirmed by professional physicians according to the inpatient medical records.

Statistical analysis

Normally distributed measurement data were expressed as mean + sd. Multiple pairwise-comparison between different groups was conducted using a one-way analysis of variance. The least significant difference (LSD) test and Dunnett’s T3 test were used for evaluating the homogeneity of variance and heterogeneity of variance, respectively. Non-normally distributed data were presented as median and centiles (25th and 75th), while the comparison between the groups was performed using the Kruskal-Wallis rank sum test. Enumeration data were presented as frequency and percentage (n, %), and comparisons between groups were performed by the chi-square test. The Kaplan-Meier method was used to calculate the incidence of HF events in each group and the overall population, and a log-rank test was adopted to compare the difference in the incidence of HF.

eGFRcys and eGFRcr were categorized into the following four groups (ml/min/1.73m2 ): ≥90, 60–89, 45–59, ≤ 45.The eGFRdiff was assessed as a categorical and continuous variable. The effect of different eGFR groups and each 15 increases in eGFR on new-onset HF was studied using a multivariate Cox stepwise regression model. Model 1 unadjusted. Model 2 was adjusted for age and sex. Model 3 was further adjusted for SBP, BMI, TC, HbA1c, hemoglobin, smoking, anti-diabetic treatment, antihypertensive treatment, MI, and atrial fibrillation.

In addition, based on Model 1(age, sex), Model 2 (ARIC-sans-BNP model: age, sex, HR, BMI, SBP, HbA1c, hypertension and MI), C-Statistic, net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to assess the ability of different eGFR to improve HF prediction models, respectively.

In order to avoid the influence of MI and hypertension on HF, sensitivity analysis was performed after excluding the above population.

SAS version 9.4 was used for the analysis (SAS Institute, Cary, NC, USA). All statistical analyses were double-tailed, with statistical significance set at P < 0.05.

Results

We included 7,967 participants who met the diagnostic criteria for T2D and underwent the 6th health examination as well as cystatin C and serum creatinine investigations.

Baseline characteristics

At baseline, the participants’ average age was 60.51 ± 10.03 years, with 6,300 (79.08%) males. The average systolic blood pressure (SBP) and HbA1c were 147.27 ± 20.58 mmHg and 7.57 ± 1.66%, respectively. The eGFRcys, eGFRcr, and eGFRdiff were 88.77 ± 22.73 mL/min*1.73 m2, 93.41 ± 15.43 mL/min*1.73 m2, and − 4.59 ± 18.72 mL/min*1.73 m2, respectively. The eGFR evaluated by serum creatinine and cystatin C showed inconsistent results, with eGFRcys values generally being lower than eGFRcr.

More than half of participants had a baseline eGFRdiff between − 15 and 15 mL/min/1.73 m2 (4679 participants [58.7%]; midrange eGFRdiff); 2280 participants (28.6%) had an eGFRdiff less than − 15 mL/min/1.73 m2 (negative eGFRdiff ), and 1008 participants (12.6%) had an eGFRdiff of 15 mL/min/1.73 m2 or greater (positive eGFRdiff). Compared with the other 2 eGFRdiff groups, participants in the negative eGFRdiff group were older, more often female, with higher baseline SBP, BMI, uACR and more anti-diabetic and antihypertensive treatment (Table 1; Fig. 1).

Table 1 Baseline characteristics overall and by eGFRdiff categories in participants

	overall
7967	<-15 ml/min/1.73m2
2280	-15 ~ 15 ml/min/1.73m2
4679	≥ 15 ml/min/1.73m2
1008	P value	
Male, n (%)	6300(79.08)	1728(75.79)	3723(79.57)	849(84.23)	< 0.001	
Age, years	60.51 ± 10.03	62.44 ± 10.06	59.61 ± 10.12	60.31 ± 8.88	< 0.001	
eGFRcr, ml/min/1.73m2	93.41 ± 15.43	93.63 ± 14.92	94.84 ± 15.66	86.26 ± 13.45	< 0.001	
eGFRcys, ml/min/1.73m2	88.77 ± 22.73	67.96 ± 16.92	93.69 ± 17.74	113.03 ± 17.15	< 0.001	
SBP, mmHg	147.27 ± 20.58	149.47 ± 21.34	146.51 ± 20.21	145.81 ± 20.19	< 0.001	
DBP, mmHg	82.53 ± 10.81	82.52 ± 11.35	82.56 ± 10.64	82.40 ± 10.49	0.928	
HR, bpm	79.33 ± 12.74	80.05 ± 13.26	79.09 ± 12.43	78.38 ± 12.74	0.040	
BMI, kg/mm2	25.83 ± 3.47	26.13 ± 3.57	25.79 ± 3.42	25.28 ± 3.36	< 0.001	
Waist circumference, cm	88.11 ± 7.16	88.60 ± 7.38	87.98 ± 7.04	87.55 ± 7.15	0.093	
TC, mmol/L	5.52 ± 1.15	5.56 ± 1.14	5.52 ± 1.14	5.44 ± 1.20	0.026	
HDL-C*, mmol/L	1.39(1.19 ~ 1.63)	1.36(1.15 ~ 1.60)	1.40(1.21 ~ 1.65)	1.40(1.21 ~ 1.68)	0.091	
LDL-C, mmol/L	3.25 ± 0.96	3.16 ± 0.88	3.24 ± 0.95	3.48 ± 1.11	< 0.001	
FBG, mmol/L	9.08 ± 3.18	9.06 ± 3.15	9.07 ± 3.15	9.03 ± 3.30	0.776	
HbA1c, %	7.57 ± 1.66	7.60 ± 1.60	7.56 ± 1.68	7.50 ± 1.67	0.231	
Hemoglobin, g/L	151.27 ± 63.57	150.20 ± 52.45	151.31 ± 69.22	153.57 ± 59.04	0.381	
hsCRP *, mg/L	1.14(0.39 ~ 2.81)	1.60(0.59 ~ 3.79)	1.03(0.35 ~ 2.57)	0.83(0.30 ~ 2.18)	0.264	
Smoking, n (%)	1361(17.08)	412(18.07)	820(17.53)	129(12.80)	0.001	
Hypertension, n (%)	4116(51.66)	2399(51.27)	1191(52.24)	526(52.18)	0.706	
Atrial fibrillation, n (%)	93(1.46)	31(1.81)	54(1.42)	8(0.93)	0.023	
MI, n (%)	168(2.38)	48(2.11)	96(2.05)	24(2.38)	0.804	
uACR, mg/mmol					< 0.001	
 < 3	5270(66.15)	1341(58.82)	3176(67.88)	753(74.70)		
 3–30	2207(27.70)	720(31.58)	1257(26.86)	230(22.82)		
 ≥ 30	490(6.15)	219(9.61)	246(5.26)	25(2.48)		
Anti-diabetic treatment, n (%)	3283(41.21)	1008(44.21)	1901(40.63)	374(37.10)	0.003	
 Insulin, n (%)	1349(16.93)	445(19.52)	751(16.05)	153(15.18)	< 0.001	
 Oral medicine, n (%)	2321(29.13)	688(30.18)	1358(29.07)	275(27.28)	0.234	
Antihypertensive treatment, n (%)	1100(13.81)	181(7.94)	248(5.3)	33(3.27)	< 0.001	
 ACEI/ARB	120(1.51)	46(2.02)	69(1.47)	5(0.05)	< 0.001	
 Beta-blocker, n (%)	86(1.08)	25(1.10)	55(1.18)	8(1.44)	0.721	
 Calcium channel blocker, n (%)	309(3.88)	14(3.37)	163(3.48)	30(8.50)	< 0.001	
 Diuretic, n (%)	69(0.87)	48(0.78)	43(1.65)	7(1.26)	0.001	
SBP: Systolic blood pressure; DBP: Diastolic blood pressure; HR: Heart rate; TC: Total cholesterol; HDL-C: high density lipoprotein-cholesterol; LDL-C: low density lipoprotein-cholesterol; FBG: Fasting blood glucose; HbA1c: Hemoglobin A1c; hs-CRP: High sensitivity C-reactive protein; MI: Myocardial infarction; uACR: urine albumin-to-urine creatinine ratio; ACEI: Angiotensin-converting enzyme inhibitor; ARB: Angiotensin receptor blocker; * expressed in M(Q1 ~ Q3)

Fig. 1 A: Distribution of eGFRcr and eGFRcys in the total population; B: Distribution of eGFRdiff in the total population

Cumulative incidence of HF events by eGFRdiff groups

Following a median follow-up time of 3.76 ± 0.72 years, 172 patients (2.16%) developed HF, and 517 patients (6.49%) died of all-cause mortality, respectively. The cumulative HF incidence in the midrange, negative, and positive eGFRdiff groups was 1.74%, 4.10%, and 0.61%, respectively. A log-rank test showed a significant difference in the cumulative incidence between the three groups (Fig. 2).

Fig. 2 Incidence of heart failure by eGFRdiff category: lower than − 15 mL/min/1.73 m2, -15 to 15 mL/min/1.73 m2, 15 mL/min/1.73 m2 or greater. p < 0.01 for differences among curves using the log-rank test

Multivariate cox regression analysis of the relationship between eGFR and new-onset HF

Using HF occurrence and grouping by different eGFR measures as the dependent and independent variables, the multivariate Cox regression analysis made adjustments for all traditional cardiovascular disease risk factors. The results showed that compared to the normal eGFRcr group, the HF risk increased progressively with reducing eGFRcr (HR values 1.84–3.61, all p < 0.05). Similarly, the HF risk increased progressively with decreasing eGFRcys, compared to the normal eGFRcys group (HR values 3.21–10.84, all p < 0.05). Moreover, the negative eGFRdiff group displayed a significantly increased risk of HF (HR 2.15; 95% CI: 1.57–2.94), while the positive eGFRdiff group had a significantly reduced HF risk when compared to the midrange eGFRdiff group (HR 0.40; 95% CI: 0.17–0.93). And each 15 mL/min/ 1.73 m2 higher eGFRdiff was associated with 34% (HR, 0.66; 95% CI, 0.58 − 0.47)lower risk of incident HF.

Table 2 Hazard ratios (HR) and 95% confidence intervals of different eGFR for HF

	No.	Median Follow-up
(years)	Incident Heart Failure(%)	incidence Rate
(/1000 person-years)	Model1	Model 2	Model 3#	
eGFRcr	
 > 90	5133	4.05	66(1.29)	3.29	1	1	1	
 60–90	2618	3.66	85(3.25)	9.19	2.83(2.05, 3.92)	1.88(1.31, 2.71)	1.84(1.18, 2.81)	
 45 ~ 60	165	3.45	14(8.48)	26.34	8.11(4.55, 14.47)	4.26(2.25, 8.06)	1.67(0.67, 4.18)	
 < 45	51	3.26	7(13.73)	48.56	14.91(6.83, 32.54)	9.41(4.21, 21.06)	3.61(1.27, 10.31)	
eGFRcys	
 > 90	4005	3.80	27(0.67)	1.77	1	1	1	
 60–90	3088	3.97	81(2.62)	6.91	3.89(2.52, 6.02)	3.18(2.03, 4.99)	3.21(1.89, 5.45)	
 45 ~ 60	620	3.76	35(5.65)	15.75	8.88(2.52, 6.02)	6.14(3.56, 10.59)	5.39(2.83, 10.28)	
 < 45	254	3.74	29(11.42)	34.30	19.32(11.43, 32.64)	13.20(7.45, 23.38)	10.84(5.32, 22.07)	
eGFRdiff								
 <-15	4703	4.11	76(3.22)	10.35	2.45(1.81, 3.34)	2.07(1.52, 2.83)	2.15(1.57, 2.94)	
 -15 ~ 15	2159	3.85	90(1.18)	4.22	1	1	1	
 ≥ 15	1015	3.47	6(10.79)	1.74	0.41(0.18, 0.94)	0.40(0.18, 0.92)	0.40(0.17, 0.93)	
eGFRdiff Per + 15	7967	3.83	172(2.13)	5.73	0.67(0.60, 0.75)	0.61(0.53, 0.71)	0.66(0.58, 0.74)	
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: in the eGFRcr and eGFRcys models, adjusted for age, sex, SBP, BMI, TC, HbA1c, hemoglobin, smoking, anti-diabetic treatment, antihypertensive treatment, MI and atrial fibrillation, eGFRdiff models adjusted for baseline eGFRcr based on the above influencing factors. eGFR in mL/min/1.73 m2

Additional predictive value of eGFR for established HF risk models

In order to explore whether adding different eGFR measures to established HF risk models could improve predictive performance, we added continuous eGFRcr, eGFRcys, and eGFRdiff variables as well as grouped eGFRdiff to model 1 (age and sex) and model 2 (ARIC-sans-BNP model [20]), respectively. Addition of eGFRcr to the ARIC-sans-BNP model led to negligible improvement, eGFRcys and eGFRdiff outperformed eGFRcr in HF prediction beyond conventional risk factors, and the best model was the addition of eGFRcys to ARIC-sans-BNP model (Table 3).

Table 3 The additional predictive value of different eGFR for HF

	C-Statistic	P-value	NRI	P-value	IDI	P-value	
Model 1	0.746(0.708, 0.783)	-	ref	-	ref	-	
Model 1 + eGFRcr	0.755(0.719, 0.792)	0.211	0.152(0.003, 0.195)	< 0.001	0.004(-0.001, 0.013)	0.364	
Model 1 + eGFRcys	0.784(0.749, 0.820)	0.002	0.171(0.014, 0.288)	< 0.001	0.012(0.001, 0.036)	< 0.001	
Model 1 + eGFRdiff	0.773(0.737, 0.810)	0.010	0.147(-0.025, 0.269)	0.182	0.008(0.001, 0.015)	< 0.001	
Model 1 + eGFRdiff group	0.747(0.709, 0.784)	0.750	0.162(-0.075, 0.209)	0.182	0.001(0.000, 0.002)	< 0.001	
Model 2	0.725(0.680, 0.771)	-	ref	-	ref	-	
Model 2 + eGFRcr	0.742(0.696, 0.789)	0.209	0.197(0.030, 0.292)	< 0.001	0.005(0.001, 0.009)	< 0.001	
Model 2 + eGFRcys	0.783(0.735, 0.831)	0.005	0.200(0.023, 0.213)	< 0.001	0.012(0.001, 0.019)	< 0.001	
Model 2 + eGFRdiff	0.766(0.719, 0.813)	0.010	0.084(-0.010, 0.182)	0.364	0.005(-0.001, 0.009)	0.182	
Model 2 + eGFRdiff group	0.725(0.679, 0.771)	0.730	0.162(-0.129, 0.233)	0.364	0.000(0.000, 0.001)	0.182	
Model 1: age, sex; Model 2 (ARIC-sans-BNP model): age, sex, HR, BMI, SBP, HbA1c, hypertension and MI. Abbreviation: NRI: net reclassification improvement; IDI: integrated discrimination improvement

Subgroup analysis

No significant interactions were observed between eGFRdiff groups and age, sex, SBP, HbA1c, BMI, uACR, hypertension, or MI status on the impact of HF (p > 0.05). Subgroup analysis results showed that HF risk decreased as eGFRdiff increased in all subgroups, consistent with the overall population results.

Sensitivity analysis

In the primary study, the participants were divided into three groups based on eGFRdiff (in mL/min/1.73 m2 ). The result display that eGFRdiff was still significantly associated with incident HF when the participants were divided into quartiles of eGFRdiff in the further study (Additional file 1:Table S1). Even after excluding individuals with hypertension or MI at baseline, the relationship persisted(Additional file 1:Table S2).

Discussion

This study found that both eGFRcr and eGFRcys can independently predict HF incidence in individuals with T2D; however, common discrepancies have been observed between these two measures. Additionally, eGFRdiff, defined as eGFRcys − eGFRcr, is also an independent predictor of incident HF. Our results showed that the negative eGFRdiff group displayed a significantly increased HF risk, while the positive eGFRdiff group had a lower HF risk, compared to the midrange eGFRdiff group. However, eGFRcys exhibits superior additional predictive value for incident HF compared to eGFRdiff and eGFRcr in population with T2D, beyond traditional heart failure risk factors.

Our population with T2D revealed poorly managed metabolic indicators like blood pressure, blood glucose, and BMI. Many patients with T2D showed significant discrepancies between eGFRcr and eGFRcys, consistent with previous studies. Subsequently, our study also confirmed the independent predictive value of both these measures for incident HF, i.e., HF risk gradually increases as eGFRcr and eGFRcys decrease. For the same degree of decline in eGFRcys and eGFRcr, the risk of GFRcys-predicted incident HF was higher than that predicted by eGFRcr. This finding corroborates the results of Chen et al. [21] in the general population. Since cystatin C is less influenced by other factors compared to creatinine, we suggest that eGFRcys might more accurately reflect glomerular filtration rate and can identify high-risk individuals for incident HF better than eGFRcr.

Due to individual discrepancies between eGFRcys and eGFRcr, recent studies have confirmed the associations of eGFRdiff with cognitive decline [22], kidney failure [23], atrial fibrillation [24], and all-cause mortality [25]. In many studies on the general population [21], hypertensive population [26], and CKD patients [27], 23.8%, 29%, and 34% of subjects had an absolute eGFRdiff > 15, respectively. Moreover, in our type 2 diabetes population, the proportion of patients with an absolute eGFRdiff > 15 was higher (41.2%), suggesting more significant discrepancies between eGFRcys and eGFRcr in Chinese patients with T2D. However, further research is needed to confirm the distribution and potential clinical significance of individual eGFRdiff in this cohort due to the paucity of relevant data.

After adjusting for relevant influencing factors, regression analysis results showed that compared to the midrange eGFRdiff group, the negative eGFRdiff group displayed a 115% increased HF risk (HR = 2.15; 95% CI: 1.57–2.94), while the positive eGFRdiff group revealed a 60% decreased HF risk (HR = 2.15;95%CI:1.57–2.94). Thus, for every 15 mL/min/1.73 m2 eGFRdiff increase, the HF risk was reduced by 34%. In the CRIC baseline study [27], the increased and decreased risk of HF in the negative and positive eGFRdiff groups was not statistically significant, compared to the midrange eGFRdiff group. However, in time-updated analyses, the negative eGFRdiff group showed enhanced HF risk (HR = 1.99; 95% CI: 1.39–2.86), and the positive eGFRdiff group displayed reduced lower HF risk (HR = 0.67; 95% CI: 0.49–0.91) compared to midrange eGFRdiff. Due to such consistent results, the impact of the discrepancy between eGFRcys and eGFRcr on new-onset HF in the diabetic population was more pronounced than in the CKD population. This may be related to the baseline differences in eGFRcr and eGFRcys levels between the two populations; however, further research is needed to verify this finding.

Because 15 mL/min/1.73 m2 represents a clinically meaningful difference in eGFR that also distinguishes CKD stages, eGFRdiff was categorized based on a cutoff point of 15. In the sensitivity analysis, our results (Supplemental Table S1), which are in line with the conclusions of Carrero et al. [14], were identical even after grouping participants into eGFRdiff-based quartiles and repeating the Cox analysis with each additional standard deviation. Since hypertension and myocardial infarction are two major causes of HF, we performed a sensitivity analysis excluding these two groups to minimize their impact. Nevertheless, the results remained consistent (Supplementary Table S1). Due to gender imbalance and poor metabolic control in this population, we repeated Cox regression in different subgroups and obtained consistent results across all subgroups.

We not only confirmed that different eGFR measures can independently predict HF risk in type 2 diabetes patients, but also found that adding eGFRcr, eGFRcys, and eGFRdiff to established HF risk prediction models improves their predictive ability; however, eGFRcys provides superior additional predictive value compared to eGFRdiff. To our knowledge, this is the first study to evaluate how various eGFR measures in a type 2 diabetes population can improve traditional HF prediction models. To date, only Lees et al. [25] have confirmed that eGFRcys greatly improves cardiovascular disease prediction models than eGFRdiff in the general population.

No conclusive evidence is available regarding the mechanism underlying the association between eGFRdiff and incident HF. A possible explanation suggests the presence of “Pore Shrinkage Syndrome,” in which the glomerular basement membrane’s pore size decreases. Due to the larger molecular weight of cystatin C than creatinine, glomerular filtration of cystatin C decreases while creatinine filtration is unaffected. As a result, serum cystatin C levels increase and creatinine levels remain unchanged, thereby causing reduced eGFRcys compared to eGFRcr [28]. Subsequently, elevated pro-atherogenic protein levels in such patients could lead to the occurrence and development of cardiovascular diseases [29]. Another possible explanation is “sarcopenia,” as diabetic patients are more prone to sarcopenia compared to those with normal blood glucose levels [30]. Reduced muscle mass causes lower creatinine, higher eGFRcr, and significantly negative eGFRdiff values, respectively [31]. Thus, sarcopenia and HF share common pathophysiological pathways. A muscle metaboreceptor (ergoreceptor) contributes to the hemodynamic and autonomic responses to exercise by controlling the sympathetic, hypertensive, and hyperpnoic responses to exercise and may have a role in the vicious cycle of sympathetic activation, which is considered one of the central elements of HF pathogenesis [32, 33].

Our study had some limitations. Firstly, HF events were identified based on hospitalization diagnosis codes, which may vary across hospitals and exclude non-hospitalized HF patients. Secondly, our observational study design could not establish causality. Lastly, baseline serum creatinine and cystatin C measurements were taken only once and might have caused misclassification bias. However, the large sample size, a stable cohort, detailed influencing factors, and our robust results lend high credibility to our conclusions.

Conclusion

This study confirmed the independent predictive values of different eGFR metrics for HF risk in patients with T2D. Specifically, eGFRcys demonstrates greater sensitivity in predicting HF risk and can significantly enhance the predictive capability of traditional HF models. Our findings support a comprehensive use of cystatin C to estimate eGFR clinically, thereby emphasizing the need to assess eGFRcys rather than relying solely on eGFRcr or eGFRdiff values for HF risk stratification.

Additional file 1: Table S1. The effect of eGFRdiff on heart failure by quartile and each SD increase(Sensitivity analysis). Table S2. Hazard ratios (HR) and 95% Confidence intervals of different eGFRcr for heart failure (Sensitivity analysis).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Abbreviations

HF Heart failure

T2D Type 2 diabetes

eGFR Estimated glomerular filtration rate

NRI Net reclassification improvement

IDI Integrated discrimination improvement

SD Standard deviation

BMI Body mass index

CKD-EPI Chronic Kidney Disease Epidemiology Collaboration

ADA American Diabetes Association

SBP Systolic blood pressure

DBP Diastolic blood pressure

HR Heart rate

TC Total cholesterol

HDL-C High density lipoprotein-cholesterol

LDL-C Low density lipoprotein-cholesterol

FBG Fasting blood glucose

HbA1c Hemoglobin A1c

hs-CRP High sensitivity C-reactive protein

MI Myocardial infarction

uACR urine albumin-to-urine creatinine ratio

ACEI Angiotensin-converting enzyme inhibitor

ARB Angiotensin receptor blocker

ACEI Angiotensin-converting enzyme inhibitor

ARB Angiotensin receptor blocker

WATCH-DM Age, sex, BMI, SBP, DBP, FPG, serum creatinine, HDL cholesterol, MI

Acknowledgements

We thank all the survey teams of the Kailuan Study Group for their contribution and the study participants who contributed their information.

Author contributions

DS wrote the manuscript. DS, WS and JT conducted the data extraction and data analysis. DS, JT, GW and QZ did the statistical analyses. SW and WG reviewed and edited the manuscript. All authors read and approved the final manuscript.

Funding

No funding.

Data availability

No datasets were generated or analysed during the current study.

Declaration

Ethics approval and consent participate

The study protocol was approved by the Ethics Committee of Kailuan General Hospital. All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dasen Sang, Jie Tao, Shouling Wu and Wei Geng contributed equally to this work.
==== Refs
References

1. Ambrosy AP Fonarow GC Butler J The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries J Am Coll Cardiol 2014 63 12 1123 33 10.1016/j.jacc.2013.11.053 24491689
Ambrosy AP, Fonarow GC, Butler J, et al. The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol. 2014;63(12):1123–33.24491689 10.1016/j.jacc.2013.11.053
2. McMurray JJ Adamopoulos S Anker SD ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the Task Force for the diagnosis and treatment of Acute and Chronic Heart failure 2012 of the European Society of Cardiology.Developed in collaboration with the Heart Failure Association of the ESC.Eur Heart J 2012 33 14 1787 847 10.1093/eurheartj/ehs104
McMurray JJ, Adamopoulos S, Anker SD, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the Task Force for the diagnosis and treatment of Acute and Chronic Heart failure 2012 of the European Society of Cardiology.Developed in collaboration with the Heart Failure Association of the ESC.Eur. Heart J. 2012;33(14):1787–847.10.1093/eurheartj/ehs104
3. Roger VL Epidemiology of heart failure Circ Res 2013 113 6 646 59 10.1161/CIRCRESAHA.113.300268 23989710
Roger VL. Epidemiology of heart failure. Circ Res. 2013;113(6):646–59.23989710 10.1161/CIRCRESAHA.113.300268
4. Hao G Wang X Chen Z Prevalence of heart failure and left ventricular dysfunction in China: the China Hypertension Survey, 2012–2015 Eur J Heart Fail 2019 21 11 1329 13137 10.1002/ejhf.1629 31746111
Hao G, Wang X, Chen Z, et al. Prevalence of heart failure and left ventricular dysfunction in China: the China Hypertension Survey, 2012–2015. Eur J Heart Fail. 2019;21(11):1329–13137.31746111 10.1002/ejhf.1629
5. Gu DF Huang GY He J Investigation of prevalence and distributing feature of chronic heart failure in Chinese adult population Chin J Cardiol 2003 31 1 3 6
Gu DF, Huang GY, He J, et al. Investigation of prevalence and distributing feature of chronic heart failure in Chinese adult population. Chin J Cardiol. 2003;31(1):3–6.
6. Conrad N Judge A Canoy D Temporal trends and patterns in Mortality after Incident Heart failure: a longitudinal analysis of 86 000 individuals JAMA Cardiol 2019 4 11 1102 11 10.1001/jamacardio.2019.3593 31479100
Conrad N, Judge A, Canoy D, et al. Temporal trends and patterns in Mortality after Incident Heart failure: a longitudinal analysis of 86 000 individuals. JAMA Cardiol. 2019;4(11):1102–11.31479100 10.1001/jamacardio.2019.3593
7. Maedeh KK Suzanne ORBD Worsening kidney function is the major mechanism of Heart failure in hypertension: the ALLHAT Study JACC Heart Fail 2020 9 2 100 11 33189627
Maedeh KK, Suzanne ORBD, et al. Worsening kidney function is the major mechanism of Heart failure in hypertension: the ALLHAT Study. JACC Heart Fail. 2020;9(2):100–11.33189627
8. Zelnick LR Shlipak MG Soliman EZ Prediction of Incident Heart failure in CKD: the CRIC Study Kidney Int Rep 2022 7 4 708 19 10.1016/j.ekir.2022.01.1067 35497796
Zelnick LR, Shlipak MG, Soliman EZ, et al. Prediction of Incident Heart failure in CKD: the CRIC Study. Kidney Int Rep. 2022;7(4):708–19.35497796 10.1016/j.ekir.2022.01.1067
9. Aaron MH Julia JH Jie LS Estimated glomerular filtration rate variability in patients with heart failure and chronic kidney disease J Card Fail 2021 27 11 1175 84 10.1016/j.cardfail.2021.04.016 33971291
Aaron MH, Julia JH, Jie LS, et al. Estimated glomerular filtration rate variability in patients with heart failure and chronic kidney disease. J Card Fail. 2021;27(11):1175–84.33971291 10.1016/j.cardfail.2021.04.016
10. Inker LA Eneanya ND Coresh J New creatinine- and cys- tatin C-based equations to estimate GFR without race N Engl J Med 2021 385 1737 49 10.1056/NEJMoa2102953 34554658
Inker LA, Eneanya ND, Coresh J, et al. New creatinine- and cys- tatin C-based equations to estimate GFR without race. N Engl J Med. 2021;385:1737–49.34554658 10.1056/NEJMoa2102953
11. Lees JS Welsh CE Celis-Morales CA Glomerular fil-tration rate by differing measures, albuminuria and predic- tion of cardiovascular disease, mortality and end-stage kid-ney disease Nat Med 2019 25 1753 60 10.1038/s41591-019-0627-8 31700174
Lees JS, Welsh CE, Celis-Morales CA, et al. Glomerular fil-tration rate by differing measures, albuminuria and predic- tion of cardiovascular disease, mortality and end-stage kid-ney disease. Nat Med. 2019;25:1753–60.31700174 10.1038/s41591-019-0627-8
12. Peralta CA Lee A Odden MC Association between chronic kidney disease detected using creatinine and cystatin C and death and cardiovascular events in elderly Mexican americans: the Sacramento Area latino study on aging J Am Geriatr Soc 2013 61 1 90 5 10.1111/jgs.12040 23252993
Peralta CA, Lee A, Odden MC, et al. Association between chronic kidney disease detected using creatinine and cystatin C and death and cardiovascular events in elderly Mexican americans: the Sacramento Area latino study on aging. J Am Geriatr Soc. 2013;61(1):90–5.23252993 10.1111/jgs.12040
13. Villeval’de SV Gudgalis NI Kobalava ZD Cistatin C as a novel marker of renal function impairement and cardiovascular risk Kardiologiia 2010 50 6 78 82 20659033
Villeval’de SV, Gudgalis NI, Kobalava ZD. Cistatin C as a novel marker of renal function impairement and cardiovascular risk. Kardiologiia. 2010;50(6):78–82.20659033
14. Carrero JJ Fu EL Sang Y Ballew S Discordances between creatinine- and cystatin C–Based estimated GFR and adverse clinical outcomes in routine clinical practice Am J Kidney Dis 2023 82 5 534 42 10.1053/j.ajkd.2023.04.002 37354936
Carrero JJ, Fu EL, Sang Y, Ballew S, et al. Discordances between creatinine- and cystatin C–Based estimated GFR and adverse clinical outcomes in routine clinical practice. Am J Kidney Dis. 2023;82(5):534–42.37354936 10.1053/j.ajkd.2023.04.002
15. Kim H Park JT Lee J Jung JY The difference between cystatin C- and creatinine-based eGFR is associated with adverse cardiovascular outcome in patients with chronic kidney disease Atherosclerosis 2021 335 53 61 10.1016/j.atherosclerosis.2021.08.036 34571286
Kim H, Park JT, Lee J, Jung JY, et al. The difference between cystatin C- and creatinine-based eGFR is associated with adverse cardiovascular outcome in patients with chronic kidney disease. Atherosclerosis. 2021;335:53–61.34571286 10.1016/j.atherosclerosis.2021.08.036
16. Wu S Huang Z Yang X Prevalence of ideal cardiovascular health and its relationship with the 4year cardiovascular events in a northern Chinese industrial city Circ Cardiovasc Qual Outcomes 2012 5 4 487 93 10.1161/CIRCOUTCOMES.111.963694 22787064
Wu S, Huang Z, Yang X, et al. Prevalence of ideal cardiovascular health and its relationship with the 4year cardiovascular events in a northern Chinese industrial city. Circ Cardiovasc Qual Outcomes. 2012;5(4):487–93.22787064 10.1161/CIRCOUTCOMES.111.963694
17. Inker LA Schmid CH Tighiouart H Estimating glomerular filtration rate from serum creatinine and cystatin C N Engl J Med 2012 367 20 9 10.1056/NEJMoa1114248 22762315
Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20–9.22762315 10.1056/NEJMoa1114248
18. American Diabetes Association Standards of medical care in diabetes–2010 Diabetes Care 2010 33 Suppl 1 11 61 10.2337/dc10-S011
American Diabetes Association. Standards of medical care in diabetes–2010. Diabetes Care. 2010;33(Suppl 1):11–61.10.2337/dc10-S011
19. Heart Failure Group of Chinese Society of Cardiology of Chinese Medical Association; Chinese Heart Failure Association of Chinese Medical Doctor Association. Editorial Board of Chinese Journar of Cardioloy. The guidelines for diagnosis and treatment of heart failure in China in 2018. Chin J Heart Fail Cardiomyopathy. 2018; (4):196–225.
20. Agarwal SK Chambless LE Ballantyne CM Prediction of incident heart failure in general practice: the atherosclerosis risk in communities (ARIC) Study Circ Heart Fail 2012 5 4 422 9 10.1161/CIRCHEARTFAILURE.111.964841 22589298
Agarwal SK, Chambless LE, Ballantyne CM, et al. Prediction of incident heart failure in general practice: the atherosclerosis risk in communities (ARIC) Study. Circ Heart Fail. 2012;5(4):422–9.22589298 10.1161/CIRCHEARTFAILURE.111.964841
21. Chen DC Lees JS Lu K Differential associations of Cystatin C Versus Creatinine-based kidney function with risks of Cardiovascular Event and Mortality among south Asian individuals in the UK Biobank J Am Heart Assoc 2023 12 3 e027079 10.1161/JAHA.122.027079 36695320
Chen DC, Lees JS, Lu K, et al. Differential associations of Cystatin C Versus Creatinine-based kidney function with risks of Cardiovascular Event and Mortality among south Asian individuals in the UK Biobank. J Am Heart Assoc. 2023;12(3):e027079.36695320 10.1161/JAHA.122.027079
22. Wang J Liu Y Jin R Intraindividual difference in estimated GFR by creatinine and cystatin C, cognitive trajectories and motoric cognitive risk syndrome Nephrol Dial Transpl 2024 39 5 860 72 10.1093/ndt/gfad234
Wang J, Liu Y, Jin R, et al. Intraindividual difference in estimated GFR by creatinine and cystatin C, cognitive trajectories and motoric cognitive risk syndrome. Nephrol Dial Transpl. 2024;39(5):860–72.10.1093/ndt/gfad234
23. Farrington DK Surapaneni A Matsushita K Discrepancies between Cystatin C-Based and creatinine-based eGFR Clin J Am Soc Nephrol 2023 18 9 1143 52 10.2215/CJN.0000000000000217 37339177
Farrington DK, Surapaneni A, Matsushita K, et al. Discrepancies between Cystatin C-Based and creatinine-based eGFR. Clin J Am Soc Nephrol. 2023;18(9):1143–52.37339177 10.2215/CJN.0000000000000217
24. Heo GY Koh HB Jung CY Difference between estimated GFR based on Cystatin C Versus Creatinine and Incident Atrial Fibrillation: a cohort study of the UK Biobank Am J Kidney Dis 2024 83 6 729 38 10.1053/j.ajkd.2023.11.004 38171411
Heo GY, Koh HB, Jung CY, et al. Difference between estimated GFR based on Cystatin C Versus Creatinine and Incident Atrial Fibrillation: a cohort study of the UK Biobank. Am J Kidney Dis. 2024;83(6):729–38.38171411 10.1053/j.ajkd.2023.11.004
25. Lees JS Welsh CE Celis-Morales CA Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease Nat Med 2019 25 11 1753 60 10.1038/s41591-019-0627-8 31700174
Lees JS, Welsh CE, Celis-Morales CA, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25(11):1753–60.31700174 10.1038/s41591-019-0627-8
26. Potok OA Ix JH Shlipak MG The difference between cystatin C– and creatinine-based estimated GFR and associations with Frailty and adverse outcomes: a cohort analysis of the systolic blood pressure intervention trial (SPRINT) Am J Kidney Dis 2020 76 6 765 74 10.1053/j.ajkd.2020.05.017 32682697
Potok OA, Ix JH, Shlipak MG, et al. The difference between cystatin C– and creatinine-based estimated GFR and associations with Frailty and adverse outcomes: a cohort analysis of the systolic blood pressure intervention trial (SPRINT). Am J Kidney Dis. 2020;76(6):765–74.32682697 10.1053/j.ajkd.2020.05.017
27. Chen DC Shlipak MG Scherzer R Association of intra-individual differences in estimated GFR by Creatinine Versus Cystatin C with Incident Heart failure Am J Kidney Dis 2022 80 6 762 72 10.1053/j.ajkd.2022.05.011 35817274
Chen DC, Shlipak MG, Scherzer R, et al. Association of intra-individual differences in estimated GFR by Creatinine Versus Cystatin C with Incident Heart failure. Am J Kidney Dis. 2022;80(6):762–72.35817274 10.1053/j.ajkd.2022.05.011
28. Quiroga B Ortiz A Díez J Selective glomerular hypofiltration syndrome Nephrol Dial Transpl 2023 39 1 10 7 10.1093/ndt/gfad145
Quiroga B, Ortiz A, Díez J. Selective glomerular hypofiltration syndrome. Nephrol Dial Transpl. 2023;39(1):10–7.10.1093/ndt/gfad145
29. Almén MS Björk J Nyman U Shrunken pore syndrome is Associated with increased levels of atherosclerosis promoting proteins Kidney Int Rep 2018 4 1 67 79 10.1016/j.ekir.2018.09.002 30596170
Almén MS, Björk J, Nyman U, et al. Shrunken pore syndrome is Associated with increased levels of atherosclerosis promoting proteins. Kidney Int Rep. 2018;4(1):67–79.30596170 10.1016/j.ekir.2018.09.002
30. Anagnostis P Gkekas NK Achilla C Type 2 diabetes mellitus is associated with increased risk of Sarcopenia: a systematic review and meta-analysis Calcif Tissue Int 2020 107 453 63 10.1007/s00223-020-00742-y 32772138
Anagnostis P, Gkekas NK, Achilla C, et al. Type 2 diabetes mellitus is associated with increased risk of Sarcopenia: a systematic review and meta-analysis. Calcif Tissue Int. 2020;107:453–63.32772138 10.1007/s00223-020-00742-y
31. Potok OA Ix JH Shlipak MG Cystatin C- and creatinine-based glomerular filtration rate estimation differences and Muscle Quantity and functional status in older adults: the Health, Aging, and body composition study Kidney Med 2022 4 3 100416 10.1016/j.xkme.2022.100416 35386603
Potok OA, Ix JH, Shlipak MG, et al. Cystatin C- and creatinine-based glomerular filtration rate estimation differences and Muscle Quantity and functional status in older adults: the Health, Aging, and body composition study. Kidney Med. 2022;4(3):100416.35386603 10.1016/j.xkme.2022.100416
32. Curcio F Testa G Liguori I Sarcopenia Heart Fail Nutrients 2020 12 1 211
Curcio F, Testa G, Liguori I, et al. Sarcopenia Heart Fail Nutrients. 2020;12(1):211.
33. Zuo X Li X Tang K Sarcopenia and cardiovascular diseases: a systematic review and meta-analysis J Cachexia Sarcopenia Muscle 2023 14 3 1183 98 10.1002/jcsm.13221 37002802
Zuo X, Li X, Tang K, et al. Sarcopenia and cardiovascular diseases: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2023;14(3):1183–98.37002802 10.1002/jcsm.13221
