Global Longitudinal Strain as a Sensitive Marker of Cardiometabolic Risk in the German National Cohort (NAKO)

Friday, February 6, 2026

10:36 AM - 10:43 AM

Location: Galapagos / Aruba / Ilhabela (2nd Lower Level)

Presenting Author(s)

JG

Jan Gröschel, MD

MD
Charite
Berlin, Berlin, Germany

Primary Author(s)

Clemens Ammann, MD

Physician
Charité – Universitätsmedizin Berlin
Berlin, Berlin, Germany

Co-Author(s)

SR

Susanne Rospleszcz, PhD

Principal Investigator
Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Germany
TP

Tobias Pischon, MD, MPH

Principal Investigator
Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
Berlin, Berlin, Germany
CS

Christopher Schuppert, MD

Attending Physician
Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
Freiburg, Baden-Wurttemberg, Germany
Christopher L. Schlett, MD, MPH

Deputy Director
Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
Freiburg, Baden-Wurttemberg, Germany
Jeanette Schulz-Menger, MD

Head Working Group Cardiac MRI
Charité/ University Medicine Berlin and Helios
Berlin, Berlin, Germany

Background: The German National Cohort (NAKO) is a population-based prospective study with a whole-body imaging sub-cohort comprising 30,868 participants aged 19 to 75 years who underwent standardized 3.0 T cardiovascular magnetic resonance (CMR) (1). Left ventricular (LV) global longitudinal strain (GLS) from CMR cine imaging measures systolic ventricular shortening and has emerged as a marker of early myocardial dysfunction before left ventricular ejection fraction (LVEF) declines (2,3). In the context of disease prevention, LV GLS might detect patients at risk of non-reversible myocardial tissue remodelling. The aim of this work is to investigate the cross-sectional association of cardiometabolic risk factors with LV GLS and LVEF in the NAKO study.

Methods: We analysed cine images in the long-axis two-, three- and four-chamber views of 29,710 participants in the NAKO study using artificial intelligence-assisted software (cvi42, Circle Cardiovascular Imaging, Calgary, Canada) to derive LVEF from ventricular segmentations and calculate LV GLS via myocardial feature tracking (4). Exclusions were made for poor image quality (n = 735), incomplete segmentations in any view (n = 398), physiologically implausible positive GLS values (n = 54), and outliers beyond 1.5 times interquartile range for LV GLS (n = 631) and LVEF (n = 423). Associations of cardiometabolic risk factors (5) with z-normalized (sign-reversed) LV GLS and LVEF were assessed using two multivariable linear regression models. Independent variables included self-reported hypertension or measured blood pressure ≥ 140/90 mmHg, self-reported hyperlipidaemia or LDL-cholesterol ≥ 3.0 mmol/l, diabetes or HbA1c ≥ 48 mmol/mol, kidney disease or serum creatinine ≥ 106/88 µmol/l (men/women), and current or former smoking. Additional covariates comprised age, sex, age-sex-interaction, and body mass index. Participants with missing data were omitted.

Results: After exclusions, 25,771 (44.7% women) cases were available for analysis with a mean LV GLS of 18.3 ± 2.1% and a mean LVEF of 61.3 ± 4.3%. All risk factors showed consistently negative and stronger associations with GLS than with LVEF (Figure 1). The regression models explained R² = 17.3% of the variance in LV GLS compared to R² = 8.1% in LVEF. Diabetes was associated with a decrease of β = 0.20 standard deviations (SD) in GLS and β = 0.16 SD in LVEF (Table 1). Hypertension showed opposite effects for GLS (β = −0.12) and LVEF (β = 0.06). Hyperlipidaemia was significantly inversely associated with GLS (p < 0.001) but not with LVEF (p = 0.082). Examples of participants with cardiometabolic risk factors and preserved LVEF but impaired GLS are given in Figure 2.

Conclusion: Cardiometabolic risk factors show a stronger association with LV GLS than with LVEF. Incorporating LV GLS as a complementary parameter may enhance early risk stratification in preventive cardiology.

Table 1: Standardized Beta Coefficients and Confidence Intervals from Multivariable Linear Regressions

	LV global longitudinal strain		LV ejection fraction
	Beta [95% CI]	p	Beta [95% CI]	p
Diabetes or HbA1c ≥ 48 mmol/mol	-0.20 [-0.26 – -0.15]	< 0.001	-0.16 [-0.21 – -0.10]	< 0.001
Hyperlipidaemia or LDL-c. ≥ 3.0 mmol/l	-0.10 [-0.12 – -0.07]	< 0.001	-0.02 [-0.05 – 0.00]	0.082
Hypertension or BP ≥ 140/90 mmHg	-0.12 [-0.14 – -0.09]	< 0.001	0.06 [0.03 – 0.08]	< 0.001
Kidney disease or Crea. ≥ 106/88 µmol/l	-0.11 [-0.17 – -0.06]	< 0.001	-0.11 [-0.17 – -0.05]	< 0.001
Current or former smoker	-0.11 [-0.14 – -0.09]	< 0.001	-0.06 [-0.08 – -0.03]	< 0.001
Age	-0.07 [-0.08 – -0.05]	< 0.001	0.11 [0.10 – 0.13]	< 0.001
Sex	0.64 [0.61 – 0.66]	< 0.001	0.50 [0.48 – 0.52]	< 0.001
Age×Sex	-0.06 [-0.08 – -0.03]	< 0.001	0.01 [-0.01 – 0.03]	0.433
Body mass index	-0.11 [-0.13 – -0.10]	< 0.001	0.02 [0.01 – 0.04]	< 0.001

LV: left ventricle; LDL-c: LDL-cholesterol; BP: blood pressure; Crea: serum creatinineFigure 1: Standardized Beta Coefficients and 95% Confidence Intervals from Multivariable Linear Regressions. LV: left ventricle; LDL-c: LDL-cholesterol; BP: blood pressure; Crea: serum creatinine; BMI: body mass index.

Figure 2: Examples of Participants with Preserved Left Ventricular Ejection Fraction (LVEF) and Poor Global Longitudinal Strain (GLS). First row: LVEF 54%, GLS -13%; Second row: LVEF 60%, GLS -13%.