Introduction: Malnutrition during a critical window of development in a fetus or infant can result in abnormal cardiac remodeling and function. It is uncertain whether the contribution of these effects continues to impact the cardiac remodeling and function of adults over the course of several decades of growth. Our study examined the impact of early Chinese famine exposure on cardiac remodeling, left ventricular (LV) diastolic function, and LV systolic function in adults. Methods: Participants at high risk of cardiovascular disease from the China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project (PEACE MPP) were enrolled. The famine in China lasted from 1959 to 1962. A total of three groups were formed based on the participants’ birth dates: pre-famine group, famine exposure group, and post-famine group. Logistic regression and linear mixed models were used to explore the association between famine exposure and cardiac remodeling, LV diastolic function and LV systolic function in adults. Results: The study included 2,758 participants, the mean age was 57.05 years, 62.8% were female, 26.4% had LV hypertrophy (LVH), 59.6% had LV diastolic dysfunction (LVDD), and 10.5% had reduced global longitudinal strain (GLS). Compared to post-famine exposure, participants had independently increased risk of LVH in the famine exposure group (OR: 2.02, 95% CI: 1.60–2.56) and pre-famine exposure (OR: 1.36, 95% CI: 1.06–1.76). Compared to post-famine exposure, the risk of LVDD remarkably increased in the famine exposure group (OR: 3.04, 95% CI: 2.49–3.71) and pre-famine exposure group (OR: 1.87, 95% CI: 1.52–2.31). Famine exposure had no significant impact on GLS but was associated with a significant increase in LV ejection fraction (LVEF) and LV end-diastolic diameter (LVEDD). Significant interactions were observed between the effects of famine exposure and other clinical/sociodemographic variables (gender, systolic blood pressure [SBP] ≥140 mm Hg or not, high school or above or not, and annual income <50,000 RMB or not) on these outcomes. Conclusion: Exposure to famine, particularly during fetal and infant stages, increases the risk of LVH and LVDD in adults. However, the LV systolic function remains preserved. These impacts are more pronounced in females, individuals with SBP ≥140 mm Hg, those with low income, or those with high educational status.

Cardiovascular disease (CVD) is the primary cause of mortality in adults. Alongside genetic predisposition and lifestyle choices, many factors that contribute to chronic CVD may manifest during fetal or early life stages, such as malnutrition during times of famine. Epidemiological evidence has suggested a link between famine exposure and increased CVD in adulthood [1]. The association between famine exposure and CVD is primarily linked to malnutrition and low protein levels during fetal and early life stages. However, the underlying mechanisms that connect these factors are not yet fully understood. Previous studies have revealed famine exposure leads to diseases associated with CVD, such as hypertension [2, 3], obesity [2, 4], nonalcoholic fatty liver disease [5], diabetes [6, 7], and metabolic syndrome [8]. The impact of malnutrition during fetal development and low birth weight on cardiac damage in adolescents is well established [9]. However, it is unclear whether exposure to famine during early development can lead to cardiac damage that increases the risk of left ventricular (LV) remodeling, LV diastolic dysfunction (LVDD), and LV systolic dysfunction later in life.

Although previous studies have revealed that early-life malnourishment can result in a lasting increase in the risk of CVD among adults, the specific mechanisms need more evidence [10‒12]. The number of cardiomyocytes in humans increases from the second week until the age of 20 [13]. In the terminal phase of cardiomyocyte development, as the sarcoplasmic reticulum and t-tubule membrane systems mature, cellular mass increases, resulting in mature cardiac function [14], especially diastolic function [15]. Thus, disturbances to the hearts’ growing environment during environmental perturbations during the prenatal period, infancy, and childhood (through adolescence) would be expected to directly impact cell developmental processes. In support of this, in fetuses, heart development is vulnerable to intrauterine growth retardation (IUGR), including cardiac dysfunction and remodeling [16]. These functional deficits persist into adolescence and early adulthood, causing ongoing problems for affected individuals [17, 18].

Thus, we hypothesized that early famine exposure-induced development abnormalities in cardiac function and remodeling cannot be reversed through nutritional rehabilitation and that abnormalities would be more pronounced in adulthood. Understanding the impacts of poor nutritional exposure during the critical life stages of CVD has great implications for the prevention and control of CVD.

Study Participants

The China PEACE (Patient-centered Evaluative Assessment of Cardiac Events) Million Persons Project was the first national large-scale screening project sponsored by the Chinese government aimed at identifying individuals at high risk of CVD. The protocol has been published [19]. In addition to a standard questionnaire, high-risk participants underwent an echocardiography examination and blood and urine analysis. Participants’ resident identification cards were used to determine their birth dates. Initially, 102,358 participants were screened for the sub-center study, which was conducted in eight sites across Guangdong Province from January 1, 2016 to December 31, 2020. The criteria for high risk of CVD were defined as meeting one of the following criteria: (1) history of established CVD; (2) high blood pressure; (3) dyslipidemia; (4) a 10-year risk of CVD ≥20%. We included participants at high risk of CVD who had data from a recent echocardiogram (n = 20,923). Participants were excluded if they did not have valid LVDD, GLS, or lipid data (n = 14,097) or their birth date did not correspond to one of the famine exposure periods of interest (n = 4,068). Finally, we enrolled 2,758 participants for analysis (Fig. 1). The research protocol was approved by the Central Ethics Committee at the China National Center for Cardiovascular Disease and the Ethics Committee of Guangdong Provincial People’s Hospital (No. GDREC2016438H [R2]). Additionally, written informed consent was obtained for all participants before the study.

Fig. 1.

Study flowchart.

Famine Exposure Definition

The famine in China was from 1959 to 1962. Consistent with previous studies focused on the Chinese famine [3, 20], three groups were formed based on the participants’ birth dates: the pre-famine group (born on January 1, 1954–December 31, 1957), famine-exposed group (born on January 1, 1959–December 31, 1962), and post-famine group (born on January 1, 1964–December 31, 1967).

Covariates

The sociodemographic information was collected by trained community healthcare staff using a standard questionnaire including gender, age, current smoking, drinking, education status (≥high school, <high school), economic status (annual income ≥50,000 RMB, annual income <50,000 RMB), as well as comorbidities (hypertension, coronary heart disease [CHD], diabetes, and stroke) and current medications including hypoglycemic and anti-hypertensive drugs. The body surface area (BSA) was determined as BSA (m2) = (Weight [kg]0.425 * Height [cm]0.725) * 0.007184. Body mass index (BMI) was defined as weight in kilograms divided by height in squared meters. The participants were considered obese when BMI ≥28 kg/m2 [21]. The measurement of blood pressure was according to the guideline recommendation by trained community healthcare staff using a validated automated blood pressure monitor (Omron HEM-7430, Omron Corporation, Kyoto, Japan). Hypertension [22] was defined as those who reported having hypertension or systolic blood pressure (SBP) ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or the use of antihypertensive medicine within 2 weeks until December 31, 2020. Fasting blood glucose (FBG) was measured from fingertip blood samples using the BeneCheck BK6–20M Multi-Monitoring System (Suzhou Pu Chun Tang Biotechnology, China). Type 2 diabetes mellitus was defined as those who self-reported using glucose-lowering drugs or having an FBG level ≥126 mg/dL [23]. Common lipid profiles were measured by a rapid lipid analyzer, including total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (CardioChek PA Analyzer; Polymer Technology Systems, Indianapolis, Indiana, USA).

Echocardiographic Examination

The experienced echocardiographers were trained with echocardiographic examination protocol before performing any tests on study participants. The imaging should include M-mode and 2-D measurements, Doppler flow parameters, and tissue Doppler imaging according to guideline recommendations. Interventricular septal end-diastolic thickness (IVS), LV posterior wall end-diastolic thickness (LVPW), LV end-diastolic diameter (LVEDD), and LV ejection fraction (LVEF) were acquired at the level of the mitral valve leaflets from the parasternal long-axis view. From the 4-chamber view, we acquired left atrial volume (LAV), peak E wave, and A wave of mitral inflow velocity. Tissue Doppler imaging was acquired at the interventricular septum and lateral wall of the mitral annulus including peak early systolic tissue velocity (s′) and peak early diastolic tissue velocity (e′). LAV index (LAVI) was calculated by index BSA. E/A ratio, septal e′ velocity, lateral e′ velocity, average E/e′ ratio, and LAVI were used to evaluate LVDD. Left ventricular GLS was measured from the apical 4-chamber, 2-chamber, and 3-chamber views using standard methodologies (Echo PAC201; GE Ving-Med) [24]. We use the absolute value of GLS in the text.

Study Outcomes Evaluation

LV mass (LVM) was defined as: LVM = 0.8* 1.04 *([IVS + LVEDD + LVPW]3 − LVEDD3) + 0.6 g [25]. Relative wall thickness (RWT) was defined as RWT= (LVPW * 2)/LVEDD. We calculated the LVM index (LVMI) by index height2.7. LV hypertrophy (LVH) was defined as LVMI >50 g/m2.7 (male) or >47 g/m2.7 (female) [26]. RWT >0.42 combined with LVH was defined as concentric LVH. RWT ≤0.42 combined with LVH was defined as eccentric LVH.

In the study, we used 2009 Echocardiography guidelines recommendations to evaluate LVDD [25]. Among septal e′ is <8 cm/s, lateral e′ is <10 cm/s, and LAVI ≥34 mL/m2, the presence of 2 abnormal measurements was defined as LVDD. The LVDD grade was defined as (1) grade I: the mitral E/A <0.8, deceleration time >200 ms, the average E/e′ ratio is <8 (septal and lateral); (2) grade II: the mitral E/A ratio is 0.8–1.5 and decreases by ≥50% during the Valsalva maneuver, deceleration time is from 160 to 200 ms, the average E/e′ ratio is 9–12; (3) grade III: the E/A ratio ≥2, deceleration time <160 ms, average E/e′ ratio >13. In this study, the proportion of grade II (13.4%) or III (0.3%) diastolic dysfunction was relatively low. Thus, we divided the participants into 2 groups for analysis: the LVDD group (including grade I–III) (59.6%) and the normal group. Subclinical LV systolic dysfunction was defined as GLS <16%.

Statistical Analysis

Data were expressed as mean (standard deviation) and number (proportion) for continuous variables and categorical variables, respectively. We compare the differences of continuous variables across groups using analysis of variance (ANOVA), and the Bonferroni method was applied to correct the value of p when comparing the multiple groups. Categorical characteristics were compared using the χ2 test, and the Bonferroni method was applied to correct the value of p when comparing the multiple groups. Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of famine exposure and LVH (yes vs. no), LV diastolic function (abnormal vs. normal), GLS (<16% vs. ≥16%) were calculated using multivariate logistic regression analysis, adjusting for potential confounders including age, gender, educational status (≥high school vs. <high school), economic status (annual income ≥50,000 RMB vs. annual income <50,000 RMB), SBP, diastolic blood pressure, heart rate, BMI, low-density lipoprotein cholesterol, FBG, stroke, CHD, hypertension, diabetes. Linear mixed models were used to assess associations between famine exposure and LV structure and function parameters including LVMI, LVEDD/BSA, septal-e′, and LVEF. Subgroup analyses were performed based on gender, SBP, obesity, education status, and economic status. The interaction term was added to estimate the effect of famine exposure on LVH or LVDD in subgroup analysis. The association between famine exposure and eccentric LVH, concentric LVH, or combination of LVH, LVDD, and reduced GLS was also explored by logistic regression. A two-sided p < 0.05 was considered statistically significant. SPSS 27.0 software package had been used to conduct all the statistical analyses.

Characteristics of Study Population

Among the 2,758 participants included in the study, the mean age was 57.05 years, of which 62.8% were female, 21.1% had high school or above education status, 48.6% had an annual income of more than RMB 50,000, 60.6% had hypertension, 20.1% had diabetes, 17.2% had obesity, 1.1% had stroke, and 2.8% had CHD. Of participants included, 730 (26.4%) had LVH, 1,645 (59.6%) had LVDD, and 291 (10.5%) had reduced GLS.

Left Ventricular Structure and Function Parameters and Risk Factors

The LV structure parameters included LVEDD, RWT, and LVMI. Those with LVH, LVDD, and reduced GLS had higher LVEDD and LVMI. The LV systolic function included septal s′, GLS, and LVEF. Those with LVDD and reduced GLS had lower LVEF, GLS, and septal s′. The LV diastolic function included E/A, septal e′, E/e′ ratio, and LAVI. Those with LVH, LVDD, and reduced GLS had lower E/A ratio and septal e′.

Compared to non-LVH, those with LVH tended to be older, were more likely to be female, had lower educational status, lower annual income, higher blood pressure, higher BMI, and a higher incidence of diabetes. Compared to non-LVDD, those with LVDD tended to be older, had lower educational status, higher blood pressure, higher BMI, and a higher incidence of diabetes. Compared to those with GLS ≥16%, those with reduced GLS would be more likely to be male, have higher blood pressure, higher BMI, and a higher incidence of diabetes (Table 1).

Table 1.

Baseline characteristics between participants

TotalNon-LVHLVHNon-LVDDLVDDGLS ≥16%GLS <16%
N 2,758 2,028 730 1,113 1,645 2,467 291 
Age, years 57.05±4.38 56.81±4.37 57.72±4.34* 56.06±4.37 57.73±4.25* 57.02±4.37 57.31±4.46 
Gender – female, n (%) 1,733 (62.8) 1,206 (59.5) 527 (72.2)* 697 (62.6) 1,036 (63) 1,592 (64.5) 141 (48.5)* 
Educational status (high school or above), n (%) 583 (21.1) 474 (23.4) 109 (14.9)* 269 (24.2) 314 (19.1)* 529 (21.4) 54 (18.6) 
Economic status (annual income ≥50,000 RMB), n (%) 1,340 (48.6) 1,036 (51.1) 304 (41.6)* 537 (48.2) 803 (48.8) 1,205 (48.8) 135 (46.4) 
Current smoking, n (%) 520 (18.9) 373 (18.4) 147 (20.1) 210 (18.9) 310 (18.8) 453 (18.4) 67 (23) 
Current drinking, n (%) 132 (4.8) 102 (5) 30 (4.1) 55 (4.9) 77 (4.7) 106 (4.3) 26 (8.9)* 
SBP, mm Hg 143.02±22.96 139.94±22.79 151.59±21.20* 137.28±22.94 146.91±22.16* 141.99±22.82 151.82±22.32* 
DBP, mm Hg 83.35±13.09 81.93±12.86 87.30±12.92* 80.01±12.61 85.61±12.93* 82.59±12.92 89.90±12.80* 
Heart rate, beat/min 80.19±11.57 80.43±11.38 79.53±12.05 79.19±11.71 80.87±11.42* 79.70±11.49 84.38±11.35* 
BMI, kg/m2 24.96±3.34 24.30±3.05 26.82±3.43* 23.97±3.04 25.64±3.38* 24.75±3.26 26.77±3.53* 
TC, mmol/ 5.73±1.46 5.75±1.46 5.66±1.45 5.78±1.42 5.69±1.48 5.74±1.45 5.61±1.55 
TG, mmol/L 1.91±1.09 1.86±1.07 2.04±1.15* 1.74±1.01 2.02±1.13* 1.87±1.08 2.21±1.17* 
HDL-C, mmol/L 1.56±0.47 1.56±0.48 1.55±0.43 1.64±0.49 1.50±0.45* 1.57±0.47 1.41±0.40* 
LDL-C, mmol/L 3.38±1.18 3.42±1.18 3.28±1.17* 3.44±1.15 3.34±1.20* 3.39±1.18 3.29±1.16 
FBG, mmol/L 6.13±1.85 6.22±1.96 5.88±1.51* 6.12±1.80 6.14±1.89 6.15±1.88 5.97±1.63 
Hypertension, n (%) 1,672 (60.6) 1,111 (54.8) 561 (76.8)* 545 (49) 1,127 (68.5)* 1,451 (58.8) 221 (75.9)* 
Diabetes, n (%) 553 (20.1) 378 (18.6) 175 (24)* 161 (14.5) 392 (23.8)* 479 (19.4) 74 (25.4)* 
Obesity, n (%) 475 (17.2) 233 (11.5) 242 (33.2%)* 109 (9.8) 366 (22.2)* 378 (15.3) 194 (33.3)* 
Stroke, n (%) 30 (1.1) 24 (1.2) 6 (0.8) 7 (0.6) 23 (1.4) 29 (1.2) 1 (0.3) 
CHD, n (%) 77 (2.8) 50 (2.5) 27 (3.7) 23 (2.1) 54 (3.3) 66 (2.7) 11 (3.8) 
Antihypertensive drug, n (%) 792 (28.7) 488 (24.06) 304 (41.6)* 219 (19.6) 573 (34.8)* 687 (43.9) 105 (45.7) 
Hypoglycemic drug, n (%) 221 (8.0) 142 (7.0) 79 (10.8) 49 (4.4) 172 (10.4)* 187 (39) 34 (45.9) 
Echocardiography 
 LVEDD, mm 45.47±3.71 44.62±3.35 47.84±3.65* 45.10±3.45 45.73±3.86* 45.36±3.61 46.42±4.34* 
 IVS, mm 9.75±1.37 9.32±1.12 10.94±1.32* 9.41±1.19 9.98±1.44* 9.67±1.33 10.41±1.59* 
 LVPW, mm 9.49±1.19 9.12±0.96 10.51±1.15* 9.19±1.05 9.70±1.22* 9.41±1.14 10.14±1.39* 
 LVMI, g/m2.7 43.34±10.24 38.59±5.91 56.52±7.90* 40.36±8.31 45.35±10.90* 42.83±9.79 47.64±12.67* 
 RWT 0.41±0.05 0.41±0.04 0.44±0.05* 0.40±0.04 0.42±0.05* 0.41±0.05 0.43±0.06* 
 LVEF, % 68.91±5.67 68.99±5.46 68.69±6.22 69.42±5.33 68.57±5.87* 69.17±5.50 66.77±6.57* 
 GLS, % 19.58±2.85 19.76±2.76 19.06±3.03* 20.54±2.71 18.93±2.75* 20.21±2.24 14.24±1.57* 
 Sep s′, cm/s 7.23±1.34 7.34±1.35 6.93±1.30* 7.58±1.32 6.99±1.31* 7.28±1.33 6.84±1.39* 
 E/A ratio 0.96±0.31 0.99±0.31 0.89±0.30* 1.12±0.32 0.86±0.24* 0.98±0.31 0.83±0.25* 
 Sep e′, cm/s 7.26±1.97 7.56±1.87 6.44±1.78* 9.15±1.26 5.99±1.02* 7.38±1.91 6.28±1.69* 
 E/e′ ratio 9.46±3.11 9.07±2.77 10.55±3.68* 8.29±1.91 10.26±3.49* 9.46±3.17 9.51±2.52 
 LAVI, mL/m2 25.31±7.83 24.32±7.11 28.07±8.98* 25.22±7.59 25.37±7.99 25.47±7.70 23.99±8.75* 
TotalNon-LVHLVHNon-LVDDLVDDGLS ≥16%GLS <16%
N 2,758 2,028 730 1,113 1,645 2,467 291 
Age, years 57.05±4.38 56.81±4.37 57.72±4.34* 56.06±4.37 57.73±4.25* 57.02±4.37 57.31±4.46 
Gender – female, n (%) 1,733 (62.8) 1,206 (59.5) 527 (72.2)* 697 (62.6) 1,036 (63) 1,592 (64.5) 141 (48.5)* 
Educational status (high school or above), n (%) 583 (21.1) 474 (23.4) 109 (14.9)* 269 (24.2) 314 (19.1)* 529 (21.4) 54 (18.6) 
Economic status (annual income ≥50,000 RMB), n (%) 1,340 (48.6) 1,036 (51.1) 304 (41.6)* 537 (48.2) 803 (48.8) 1,205 (48.8) 135 (46.4) 
Current smoking, n (%) 520 (18.9) 373 (18.4) 147 (20.1) 210 (18.9) 310 (18.8) 453 (18.4) 67 (23) 
Current drinking, n (%) 132 (4.8) 102 (5) 30 (4.1) 55 (4.9) 77 (4.7) 106 (4.3) 26 (8.9)* 
SBP, mm Hg 143.02±22.96 139.94±22.79 151.59±21.20* 137.28±22.94 146.91±22.16* 141.99±22.82 151.82±22.32* 
DBP, mm Hg 83.35±13.09 81.93±12.86 87.30±12.92* 80.01±12.61 85.61±12.93* 82.59±12.92 89.90±12.80* 
Heart rate, beat/min 80.19±11.57 80.43±11.38 79.53±12.05 79.19±11.71 80.87±11.42* 79.70±11.49 84.38±11.35* 
BMI, kg/m2 24.96±3.34 24.30±3.05 26.82±3.43* 23.97±3.04 25.64±3.38* 24.75±3.26 26.77±3.53* 
TC, mmol/ 5.73±1.46 5.75±1.46 5.66±1.45 5.78±1.42 5.69±1.48 5.74±1.45 5.61±1.55 
TG, mmol/L 1.91±1.09 1.86±1.07 2.04±1.15* 1.74±1.01 2.02±1.13* 1.87±1.08 2.21±1.17* 
HDL-C, mmol/L 1.56±0.47 1.56±0.48 1.55±0.43 1.64±0.49 1.50±0.45* 1.57±0.47 1.41±0.40* 
LDL-C, mmol/L 3.38±1.18 3.42±1.18 3.28±1.17* 3.44±1.15 3.34±1.20* 3.39±1.18 3.29±1.16 
FBG, mmol/L 6.13±1.85 6.22±1.96 5.88±1.51* 6.12±1.80 6.14±1.89 6.15±1.88 5.97±1.63 
Hypertension, n (%) 1,672 (60.6) 1,111 (54.8) 561 (76.8)* 545 (49) 1,127 (68.5)* 1,451 (58.8) 221 (75.9)* 
Diabetes, n (%) 553 (20.1) 378 (18.6) 175 (24)* 161 (14.5) 392 (23.8)* 479 (19.4) 74 (25.4)* 
Obesity, n (%) 475 (17.2) 233 (11.5) 242 (33.2%)* 109 (9.8) 366 (22.2)* 378 (15.3) 194 (33.3)* 
Stroke, n (%) 30 (1.1) 24 (1.2) 6 (0.8) 7 (0.6) 23 (1.4) 29 (1.2) 1 (0.3) 
CHD, n (%) 77 (2.8) 50 (2.5) 27 (3.7) 23 (2.1) 54 (3.3) 66 (2.7) 11 (3.8) 
Antihypertensive drug, n (%) 792 (28.7) 488 (24.06) 304 (41.6)* 219 (19.6) 573 (34.8)* 687 (43.9) 105 (45.7) 
Hypoglycemic drug, n (%) 221 (8.0) 142 (7.0) 79 (10.8) 49 (4.4) 172 (10.4)* 187 (39) 34 (45.9) 
Echocardiography 
 LVEDD, mm 45.47±3.71 44.62±3.35 47.84±3.65* 45.10±3.45 45.73±3.86* 45.36±3.61 46.42±4.34* 
 IVS, mm 9.75±1.37 9.32±1.12 10.94±1.32* 9.41±1.19 9.98±1.44* 9.67±1.33 10.41±1.59* 
 LVPW, mm 9.49±1.19 9.12±0.96 10.51±1.15* 9.19±1.05 9.70±1.22* 9.41±1.14 10.14±1.39* 
 LVMI, g/m2.7 43.34±10.24 38.59±5.91 56.52±7.90* 40.36±8.31 45.35±10.90* 42.83±9.79 47.64±12.67* 
 RWT 0.41±0.05 0.41±0.04 0.44±0.05* 0.40±0.04 0.42±0.05* 0.41±0.05 0.43±0.06* 
 LVEF, % 68.91±5.67 68.99±5.46 68.69±6.22 69.42±5.33 68.57±5.87* 69.17±5.50 66.77±6.57* 
 GLS, % 19.58±2.85 19.76±2.76 19.06±3.03* 20.54±2.71 18.93±2.75* 20.21±2.24 14.24±1.57* 
 Sep s′, cm/s 7.23±1.34 7.34±1.35 6.93±1.30* 7.58±1.32 6.99±1.31* 7.28±1.33 6.84±1.39* 
 E/A ratio 0.96±0.31 0.99±0.31 0.89±0.30* 1.12±0.32 0.86±0.24* 0.98±0.31 0.83±0.25* 
 Sep e′, cm/s 7.26±1.97 7.56±1.87 6.44±1.78* 9.15±1.26 5.99±1.02* 7.38±1.91 6.28±1.69* 
 E/e′ ratio 9.46±3.11 9.07±2.77 10.55±3.68* 8.29±1.91 10.26±3.49* 9.46±3.17 9.51±2.52 
 LAVI, mL/m2 25.31±7.83 24.32±7.11 28.07±8.98* 25.22±7.59 25.37±7.99 25.47±7.70 23.99±8.75* 

Continuous variables were expressed as the mean ± standard deviation (SD), and categorical variables were described as frequencies and percentages.

LVH, left ventricular hypertrophy; LVMI, left ventricular mass index; IVS, inter-ventricular septum; LVPW, left ventricular posterior wall; LVEDD, left ventricular end diastolic diameter; LVEF, left ventricular ejection fraction; GLS, global longitudinal strain; LAVI, left atrial volume index; LVDD, left ventricular diastolic dysfunction; CHD, coronary heart disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting blood glucose; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index.

*Compared with non-LVH, non-LVDD, GLS ≥16%, p < 0.05.

Association of Famine Exposure and LV Structure and Function Parameters

The prevalence of LVH in post-famine exposure, famine exposure, and pre-famine exposure were 21%, 25.9%, and 31.4%, respectively (Table 2). The prevalence of LVDD was 47.1%, 60.8%, and 69.4%, respectively. The prevalence of reduced GLS was 10.2%, 10.2%, and 11%, respectively (Fig. 2). The participants with early famine exposure had a significantly high proportion of LVH and LVDD but not subclinical systolic dysfunction. Pre-famine exposure individuals were older, had lower education status, and had more hypertension and CHD. However, metabolic-related parameters like BMI, lipid parameters, glucose, and obesity showed no difference between the famine and pre-famine exposure groups (Table 2).

Table 2.

Baseline characteristics among different famine exposure groups

TotalPost-famine groupFamine groupPre-famine groupp value
N 2,758 908 772 1,078  
Age, years 57.05±4.38 51.89±1.28 56.55±1.32a 61.77±1.23a,b <0.001 
Gender – female (%) 1,733 (62.8) 560 (61.7) 510 (66.1) 663 (61.5) 0.091 
Educational status (high school or above), n (%) 583 (21.1) 200 (22.0) 182 (23.6 201 (18.6)b 0.027 
Economic status (annual income ≥50,000 RMB), n (%) 1,340 (48.6) 461 (50.8) 386 (50) 493 (45.7) 0.053 
Current smoking, n (%) 520 (18.9) 169 (18.6) 136 (17.6) 215 (19.9) 0.440 
Current drinking, n (%) 132 (4.8) 48 (5.3) 40 (5.2) 44 (4.1) 0.384 
SBP, mm Hg 143.02±22.96 140.50±22.89 142.32±23.09a 145.65±22.67a <0.001 
DBP, mm Hg 83.35±13.09 84.30±13.81 83.25±13.22 82.62±12.32a 0.016 
Heart rate, beat/min 80.19±11.57 80.38±11.14 80.13±11.59 80.08±11.90 0.837 
BMI, kg/m2 24.96±3.34 25.10±3.32 25.08±3.45 24.77±3.28 0.047 
TC, mmol/ 5.73±1.46 5.71±1.44 5.80±1.46 5.69±1.47 0.237 
TG, mmol/L 1.91±1.09 1.89±1.10 1.96±1.14 1.88±1.05 0.281 
HDL-C, mmol/L 1.56±0.47 1.54±0.48 1.57±0.48 1.56±0.45 0.509 
LDL-C, mmol/L 3.38±1.18 3.38±1.19 3.42±1.17 3.36±1.17 0.616 
FBG, mmol/L 6.13±1.85 6.10±1.99 6.11±1.65 6.17±1.87 0.650 
Hypertension, n (%) 1,672 (60.6) 511 (56.3) 457 (59.2)a 704 (65.3)a <0.001 
Diabetes, n (%) 553 (20.1) 157 (17.3) 167 (21.6) 229 (21.2) 0.037 
Obesity, n (%) 475 (17.2) 156 (17.2) 152 (19.7) 167 (15.5) 0.064 
Stroke, n (%) 30 (1.1) 5 (0.6) 10 (1.3) 15 (1.4) 0.165 
CHD, n (%) 77 (2.8) 14 (1.5) 19 (2.4) 44 (4.0)a 0.002 
Antihypertensive drug, n (%) 792 (28.7) 202 (36.7) 217 (43.5) 373 (50.1)a <0.001 
Hypoglycemic drug, n (%) 221 (8.0) 51 (32.5) 65 (38.9) 105 (45.9)a 0.030 
Echocardiography 
 LVEDD, mm 45.47±3.71 45.40±3.65 45.21±3.61 45.72±3.82b 0.012 
 IVS, mm 9.75±1.37 9.66±1.31 9.69±1.40 9.86±1.40a,b 0.002 
 LVPW, mm 9.49±1.19 9.39±1.19 9.44±1.17† 9.61±1.18a,b <0.001 
 LVMI, g/m2.7 43.34±10.24 42.02±9.84 42.73±10.11 44.88±10.46a,b <0.001 
 RWT 0.41±0.05 0.41±0.05 0.41±0.05 0.42±0.05a 0.02 
 LVH, n (%) 730 (26.4) 191 (21) 200 (25.9)a 339 (31.4)a,b <0.001 
 LVEF, % 68.91±5.67 68.96±5.55 68.77±5.47 68.98±5.91 0.707 
 GLS, % 19.58±2.85 19.62±2.85 19.67±2.84 19.47±2.85 0.306 
 GLS <16%, n (%) 291 (10.5) 93 (10.2) 79 (10.2) 119 (11) 0.804 
 Sep s′, cm/s 7.23±1.34 7.40±1.30 7.21±1.39† 7.10±1.34a,b <0.001 
 E/A ratio 0.96±0.31 1.08±0.33 0.97±0.30† 0.87±0.26a,b <0.001 
 Sep e′, cm/s 7.26±1.97 7.88±1.97 7.21±1.85† 6.78±1.77a,b <0.001 
 E/e’ ratio 9.46±3.11 9.13±3.05 9.52±3.53 9.70±2.79a,b <0.001 
 LAVI, mL/m2 25.31±7.83 24.55±7.18 25.76±7.96 25.64±8.20a,b 0.002 
 LVDD, n (%) 1,645 (59.6) 428 (47.1) 469 (60.8)† 748 (69.4)a,b <0.001 
TotalPost-famine groupFamine groupPre-famine groupp value
N 2,758 908 772 1,078  
Age, years 57.05±4.38 51.89±1.28 56.55±1.32a 61.77±1.23a,b <0.001 
Gender – female (%) 1,733 (62.8) 560 (61.7) 510 (66.1) 663 (61.5) 0.091 
Educational status (high school or above), n (%) 583 (21.1) 200 (22.0) 182 (23.6 201 (18.6)b 0.027 
Economic status (annual income ≥50,000 RMB), n (%) 1,340 (48.6) 461 (50.8) 386 (50) 493 (45.7) 0.053 
Current smoking, n (%) 520 (18.9) 169 (18.6) 136 (17.6) 215 (19.9) 0.440 
Current drinking, n (%) 132 (4.8) 48 (5.3) 40 (5.2) 44 (4.1) 0.384 
SBP, mm Hg 143.02±22.96 140.50±22.89 142.32±23.09a 145.65±22.67a <0.001 
DBP, mm Hg 83.35±13.09 84.30±13.81 83.25±13.22 82.62±12.32a 0.016 
Heart rate, beat/min 80.19±11.57 80.38±11.14 80.13±11.59 80.08±11.90 0.837 
BMI, kg/m2 24.96±3.34 25.10±3.32 25.08±3.45 24.77±3.28 0.047 
TC, mmol/ 5.73±1.46 5.71±1.44 5.80±1.46 5.69±1.47 0.237 
TG, mmol/L 1.91±1.09 1.89±1.10 1.96±1.14 1.88±1.05 0.281 
HDL-C, mmol/L 1.56±0.47 1.54±0.48 1.57±0.48 1.56±0.45 0.509 
LDL-C, mmol/L 3.38±1.18 3.38±1.19 3.42±1.17 3.36±1.17 0.616 
FBG, mmol/L 6.13±1.85 6.10±1.99 6.11±1.65 6.17±1.87 0.650 
Hypertension, n (%) 1,672 (60.6) 511 (56.3) 457 (59.2)a 704 (65.3)a <0.001 
Diabetes, n (%) 553 (20.1) 157 (17.3) 167 (21.6) 229 (21.2) 0.037 
Obesity, n (%) 475 (17.2) 156 (17.2) 152 (19.7) 167 (15.5) 0.064 
Stroke, n (%) 30 (1.1) 5 (0.6) 10 (1.3) 15 (1.4) 0.165 
CHD, n (%) 77 (2.8) 14 (1.5) 19 (2.4) 44 (4.0)a 0.002 
Antihypertensive drug, n (%) 792 (28.7) 202 (36.7) 217 (43.5) 373 (50.1)a <0.001 
Hypoglycemic drug, n (%) 221 (8.0) 51 (32.5) 65 (38.9) 105 (45.9)a 0.030 
Echocardiography 
 LVEDD, mm 45.47±3.71 45.40±3.65 45.21±3.61 45.72±3.82b 0.012 
 IVS, mm 9.75±1.37 9.66±1.31 9.69±1.40 9.86±1.40a,b 0.002 
 LVPW, mm 9.49±1.19 9.39±1.19 9.44±1.17† 9.61±1.18a,b <0.001 
 LVMI, g/m2.7 43.34±10.24 42.02±9.84 42.73±10.11 44.88±10.46a,b <0.001 
 RWT 0.41±0.05 0.41±0.05 0.41±0.05 0.42±0.05a 0.02 
 LVH, n (%) 730 (26.4) 191 (21) 200 (25.9)a 339 (31.4)a,b <0.001 
 LVEF, % 68.91±5.67 68.96±5.55 68.77±5.47 68.98±5.91 0.707 
 GLS, % 19.58±2.85 19.62±2.85 19.67±2.84 19.47±2.85 0.306 
 GLS <16%, n (%) 291 (10.5) 93 (10.2) 79 (10.2) 119 (11) 0.804 
 Sep s′, cm/s 7.23±1.34 7.40±1.30 7.21±1.39† 7.10±1.34a,b <0.001 
 E/A ratio 0.96±0.31 1.08±0.33 0.97±0.30† 0.87±0.26a,b <0.001 
 Sep e′, cm/s 7.26±1.97 7.88±1.97 7.21±1.85† 6.78±1.77a,b <0.001 
 E/e’ ratio 9.46±3.11 9.13±3.05 9.52±3.53 9.70±2.79a,b <0.001 
 LAVI, mL/m2 25.31±7.83 24.55±7.18 25.76±7.96 25.64±8.20a,b 0.002 
 LVDD, n (%) 1,645 (59.6) 428 (47.1) 469 (60.8)† 748 (69.4)a,b <0.001 

Continuous variables were expressed as the mean ± SD, and categorical variables were described as frequencies and percentages.

LVH, left ventricular hypertrophy; LVMI, left ventricular mass index; IVS, inter-ventricular septum; LVPW, left ventricular posterior wall; LVEDD, left ventricular end diastolic diameter; LVEF, left ventricular ejection fraction; GLS, global longitudinal strain; LAVI, left atrial volume index; LVDD, left ventricular diastolic dysfunction; CHD, coronary heart disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting blood glucose; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index.

ap < 0.05 compared with the post-famine group.

bp < 0.05 compared with the famine group.

Fig. 2.

The percentage of LVH, LVDD, and reduced GLS in different famine exposure groups.

Fig. 2.

The percentage of LVH, LVDD, and reduced GLS in different famine exposure groups.

Close modal

Table 3 shows the odds ratio (95% CI) between famine exposure and LV structure and function parameters among individuals at high risk of CVD. After adjusting for confounders, compared to post-famine exposure, participants had independently increased risk of LVH in the famine exposure group (OR: 2.02, 95% CI: 1.60–2.56) and pre-famine exposure (OR: 1.36, 95% CI: 1.06–1.76), respectively, whereas those in famine exposure had a higher risk of LVH than pre-famine exposure. We also explored the association of LV structure parameters and famine exposure by linear mixed models in Table 4 and found famine exposure and pre-famine exposure had a significant positive correlation with LVMI; additionally, famine exposure had an independent positive correlation with LVEDD/BSA, but pre-famine exposure was not.

Table 3.

Association between famine exposure and left ventricular structure and function change by multivariate logistic regression analysis

Post-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for trend
LVH 
 Model I Ref 1.72 (1.40–2.11) 1.31 (1.04–1.64) <0.001 
 Model II Ref 1.73 (1.41–2.13) 1.28 (1.02–1.61) <0.001 
 Model III Ref 2.02 (1.60–2.56) 1.36 (1.06–1.76) <0.001 
LVDD 
 Model I Ref 2.54 (2.11–3.05) 1.73 (1.42–2.10) <0.001 
 Model II Ref 2.54 (2.11–3.05) 1.73 (1.42–2.10) <0.001 
 Model III Ref 3.04 (2.49–3.71) 1.87 (1.52–2.31) <0.001 
GLS <16% 
 Model I Ref 1.08 (0.81–1.44) 0.99 (0.72–1.37) 0.800 
 Model II Ref 0.47 (0.17–1.28) 0.70 (0.40–1.21) 0.344 
 Model III Ref 0.48 (0.17–1.35) 0.69 (0.39–1.23) 0.381 
Post-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for trend
LVH 
 Model I Ref 1.72 (1.40–2.11) 1.31 (1.04–1.64) <0.001 
 Model II Ref 1.73 (1.41–2.13) 1.28 (1.02–1.61) <0.001 
 Model III Ref 2.02 (1.60–2.56) 1.36 (1.06–1.76) <0.001 
LVDD 
 Model I Ref 2.54 (2.11–3.05) 1.73 (1.42–2.10) <0.001 
 Model II Ref 2.54 (2.11–3.05) 1.73 (1.42–2.10) <0.001 
 Model III Ref 3.04 (2.49–3.71) 1.87 (1.52–2.31) <0.001 
GLS <16% 
 Model I Ref 1.08 (0.81–1.44) 0.99 (0.72–1.37) 0.800 
 Model II Ref 0.47 (0.17–1.28) 0.70 (0.40–1.21) 0.344 
 Model III Ref 0.48 (0.17–1.35) 0.69 (0.39–1.23) 0.381 

Data are presented as OR and 95% CI.

OR, odds ratio; CI, confidence interval; LVH, left ventricular hypertrophy; LVDD, left ventricular diastolic dysfunction; GLS, global longitudinal strain.

Model I not adjusted.

Model II adjusted for age and gender.

Model III adjusted for age, gender, educational status, economic status, SBP, DBP, heart rate, body mass index, LDL-C, FBG, stroke, coronary heart disease, hypertension, diabetes.

Table 4.

Associations between famine exposure and left ventricular structure and function parameters by linear mixed models

LVMILVEDD/BSA
model I, β (95% CI)p valuemodel II, β (95% CI)p valuemodel I, β (95% CI)p valuemodel II, β (95% CI)p value
Post-famine group Ref  Ref  Ref  Ref  
Famine group 2.706 (0.492–4.920) 0.011 3.09 (1.18–4.99) <0.001 0.651 (0.124–1.178) 0.010 0.517 (0.026–1.009) 0.035 
Pre-famine group 3.84 (0.103–7.594) 0.042 4.38 (1.15–7.60) 0.004 0.751 (−0.141–1.643) 0.127 0.630 (−0.201–1.461) 0.196 
LVMILVEDD/BSA
model I, β (95% CI)p valuemodel II, β (95% CI)p valuemodel I, β (95% CI)p valuemodel II, β (95% CI)p value
Post-famine group Ref  Ref  Ref  Ref  
Famine group 2.706 (0.492–4.920) 0.011 3.09 (1.18–4.99) <0.001 0.651 (0.124–1.178) 0.010 0.517 (0.026–1.009) 0.035 
Pre-famine group 3.84 (0.103–7.594) 0.042 4.38 (1.15–7.60) 0.004 0.751 (−0.141–1.643) 0.127 0.630 (−0.201–1.461) 0.196 
Sep-e’LVEF
model I, β (95% CI)p valuemodel II, β (95% CI)p valuemodel I, β (95% CI)p valuemodel II, β (95% CI)p value
Post-famine group Ref  Ref  Ref  Ref  
Famine group −0.258 (−0.663–0.148) 0.339 −0.330 (−0.704–0.044) 0.102 0.886 (−0.338–2.111) 0.231 1.356 (0.134–2.577) 0.024 
Pre-famine group −0.765 (−1.451 to −0.079) 0.023 −0.855 (−1.488 to −0.222) 0.004 1.158 (−0.913–3.230) 0.452 1.956 (−0.109–4.021) 0.069 
Sep-e’LVEF
model I, β (95% CI)p valuemodel II, β (95% CI)p valuemodel I, β (95% CI)p valuemodel II, β (95% CI)p value
Post-famine group Ref  Ref  Ref  Ref  
Famine group −0.258 (−0.663–0.148) 0.339 −0.330 (−0.704–0.044) 0.102 0.886 (−0.338–2.111) 0.231 1.356 (0.134–2.577) 0.024 
Pre-famine group −0.765 (−1.451 to −0.079) 0.023 −0.855 (−1.488 to −0.222) 0.004 1.158 (−0.913–3.230) 0.452 1.956 (−0.109–4.021) 0.069 

LVMI, left ventricular mass index; LVEDD, left ventricular end diastolic diameter; BSA, body surface area; LVEF, left ventricular ejection fraction.

Model I adjusted for age and gender.

Model II adjusted for age, gender, educational status, economic status, SBP, DBP, heart rate, body mass index, LDL-C, FBG, stroke, coronary heart disease, hypertension, diabetes.

In addition, compared with the post-famine exposure, participants had remarkably increased risk of LVDD in famine exposure (OR: 3.04, 95% CI: 2.49–3.71) and pre-famine exposure (OR: 1.87, 95% CI: 1.52–2.31), respectively (Table 3). The pre-famine exposure had a significant negative correlation with septal e′ (Table 4). For subclinical systolic dysfunction, compared with post-famine exposure, participants in famine exposure and pre-famine exposure showed no different risk for reduced GLS (Table 3). Whereas, famine exposure had positively correlated with LVEF compared with post-famine exposure (Table 4). Famine exposure independently increased the risk for concentric LVH and eccentric LVH (all p < 0.05) in Table 5. However, in the LVH, LVDD, or reduced GLS combination groups, famine exposure was not an independent risk factor for these cardiac damages compared to those without cardiac damage group in Table 6, except for those with only LVH or LVDD (p < 0.05).

Table 5.

Association between famine exposure and Eccentric LVH or Concentric LVH

NPost-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for trend
Non-LVH 2,028  Ref Ref  
Eccentric LVH 294 Ref 1.98 (1.44–2.71) 1.24 (0.86–1.75) <0.001 
Concentric LVH 436 Ref 2.04 (1.53–2.72) 1.42 (1.04–1.93) <0.001 
NPost-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for trend
Non-LVH 2,028  Ref Ref  
Eccentric LVH 294 Ref 1.98 (1.44–2.71) 1.24 (0.86–1.75) <0.001 
Concentric LVH 436 Ref 2.04 (1.53–2.72) 1.42 (1.04–1.93) <0.001 

Data are presented as OR and 95% CI.

Adjusted for age, gender, educational status, economic status, SBP, DBP, heart rate, body mass index, LDL-C, FBG, stroke, coronary heart disease, hypertension, diabetes.

OR, odds ratio; CI, confidence interval; LVH, left ventricular hypertrophy.

Table 6.

Association between famine exposure and combination of abnormal LV structure and function

NPost-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for trend
Normal 890  Ref Ref  
Only LVH 169 Ref 3.17 (2.03–4.95) 1.99 (1.27–3.13) <0.001 
Only LVDD 959 Ref 3.10 (2.45–3.92) 1.92 (1.50–2.45) <0.001 
Only GLS <16% 47 Ref 0.16 (0.01–2.21) 0.38 (0.09–1.52) 0.371 
LVH and LVDD 449 Ref 2.93 (0.88–9.71) 1.73 (0.90–3.33) 0.203 
LVH and GLS <16% Ref 0.89 (0.00–1,962.85) 0.86 (0.01–55.96) 0.996 
LVDD and GLS <16% 132 Ref 1.40 (0.24–7.99) 1.55 (0.62–3.89) 0.431 
LVH and LVDD and GLS <16% 105 Ref 2.47 (0.25–23.66) 1.28 (0.37–4.34) 0.627 
NPost-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for trend
Normal 890  Ref Ref  
Only LVH 169 Ref 3.17 (2.03–4.95) 1.99 (1.27–3.13) <0.001 
Only LVDD 959 Ref 3.10 (2.45–3.92) 1.92 (1.50–2.45) <0.001 
Only GLS <16% 47 Ref 0.16 (0.01–2.21) 0.38 (0.09–1.52) 0.371 
LVH and LVDD 449 Ref 2.93 (0.88–9.71) 1.73 (0.90–3.33) 0.203 
LVH and GLS <16% Ref 0.89 (0.00–1,962.85) 0.86 (0.01–55.96) 0.996 
LVDD and GLS <16% 132 Ref 1.40 (0.24–7.99) 1.55 (0.62–3.89) 0.431 
LVH and LVDD and GLS <16% 105 Ref 2.47 (0.25–23.66) 1.28 (0.37–4.34) 0.627 

Data are presented as OR and 95% CI.

Adjusted for age, gender, educational status, economic status, SBP, DBP, heart rate, body mass index, LDL-C, FBG, stroke, coronary heart disease, hypertension, diabetes.

OR, odds ratio; CI, confidence interval; LVH, left ventricular hypertrophy; LVDD, left ventricular diastolic dysfunction; GLS, global longitudinal strain.

Subgroup Analysis

Subgroup analysis by gender, SBP, obesity, educational status, and economic status was shown in Table 7. We observed a significant modifying effect of gender, SBP, educational status, and economic status on the associations. Subgroup analysis by gender, SBP, and economic status showed that compared with the post-famine exposure group, the associations between the famine group and risk of LVH were stronger in females (OR: 2.12, 95% CI: 1.59–2.81, p for interaction <0.001) than males, in those with SBP ≥140 mm Hg (OR: 1.84, 95% CI: 1.37–2.46, p for interaction <0.001) than SBP <140 mm Hg, and in those with annual income <50,000 RMB (OR: 2.15, 95% CI: 1.56–2.97, p for interaction = 0.001) than those with annual income ≥50,000 RMB. Subgroup analysis by SBP and education status showed that compared with the post-famine exposure group, the associations between the famine group and risk of LVDD were stronger in those with SBP ≥140 mm Hg (OR: 3.25, 95% CI: 2.43–4.33, p for interaction <0.001) than SBP <140 mm Hg, in those with high school or above educational status (OR: 2.36, 95% CI: 1.52–3.65, p for interaction = 0.036) than those with below high school educational status.

Table 7.

Multivariable-adjusted subgroup between the association famine exposure and left ventricular structure and function

Post-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for interaction
LVH 
 Gender    <0.001 
  Male, n = 1,025 Ref 1.00 (0.44–2.31) 0.86 (0.21–3.44)  
  Female, n = 1,733 Ref 2.12 (1.59–2.81) 1.35 (1.00–1.83)  
 SBP    <0.001 
  SBP ≥140, mm Hg, n = 1,458 Ref 1.84 (1.37–2.46) 1.27 (0.92–1.75)  
  SBP <140, mm Hg, n = 1,300 Ref 2.59 (0.71–9.41) 1.52 (0.74–3.11)  
 Obesity    0.998 
  Yes, n = 475 Ref 19.74 (4.03–96.71) 4.29 (1.81–10.18)  
  No, n = 2,283 Ref 1.70 (0.69–4.18) 1.22 (0.74–2.03)  
 Educational status    0.358 
  High school or above, n = 583 Ref 2.47 (1.35–4.53) 1.56 (0.83–2.93)  
  Below than high school, n = 2,175 Ref 1.89 (1.46–2.43) 1.29 (0.98–1.70)  
 Economic status    0.001 
  Annual income ≥50,000 RMB, n = 1,340 Ref 1.84 (0.57–5.96) 1.21 (0.62–2.34)  
  Annual income <50,000 RMB, n = 1,418 Ref 2.15 (1.56–2.97) 1.48 (1.05–2.10)  
LVDD 
 Gender    <0.001 
  Male, n = 1,025 Ref 2.70 (0.87–8.38) 1.66 (0.91–3.04)  
  Female, n = 1,733 Ref 2.04 (0.87–4.80) 1.62 (1.01–2.59)  
 SBP    <0.001 
  SBP ≥140, mm Hg, n = 1,458 Ref 3.25 (2.43–4.33) 2.00 (1.48–2.71)  
  SBP <140, mm Hg, n = 1,300 Ref 1.42 (0.54–3.69) 1.29 (0.77–2.16)  
 Obesity    0.353 
  Yes, n = 475 Ref 0.50 (0.07–3.28) 1.12 (0.42–3.00)  
  No, n = 2,283 Ref 3.02 (2.44–3.74) 1.77 (1.41–2.22)  
 Educational status    0.036 
  High school or above, n = 583 Ref 2.36 (1.52–3.65) 1.69 (1.09–2.62)  
  Below than high school, n = 2,175 Ref 1.74 (0.79–3.80) 1.45 (0.94–2.23)  
 Economic status    0.244 
  Annual income ≥50,000 RMB, n = 1,340 Ref 1.33 (0.49–3.57) 1.29 (0.75–2.21)  
  Annual income <50,000 RMB, n = 1,418 Ref 3.15 (2.38–4.16) 1.84 (1.37–2.48)  
Post-famine group, OR (95% CI)Famine group, OR (95% CI)Pre-famine group, OR (95% CI)p for interaction
LVH 
 Gender    <0.001 
  Male, n = 1,025 Ref 1.00 (0.44–2.31) 0.86 (0.21–3.44)  
  Female, n = 1,733 Ref 2.12 (1.59–2.81) 1.35 (1.00–1.83)  
 SBP    <0.001 
  SBP ≥140, mm Hg, n = 1,458 Ref 1.84 (1.37–2.46) 1.27 (0.92–1.75)  
  SBP <140, mm Hg, n = 1,300 Ref 2.59 (0.71–9.41) 1.52 (0.74–3.11)  
 Obesity    0.998 
  Yes, n = 475 Ref 19.74 (4.03–96.71) 4.29 (1.81–10.18)  
  No, n = 2,283 Ref 1.70 (0.69–4.18) 1.22 (0.74–2.03)  
 Educational status    0.358 
  High school or above, n = 583 Ref 2.47 (1.35–4.53) 1.56 (0.83–2.93)  
  Below than high school, n = 2,175 Ref 1.89 (1.46–2.43) 1.29 (0.98–1.70)  
 Economic status    0.001 
  Annual income ≥50,000 RMB, n = 1,340 Ref 1.84 (0.57–5.96) 1.21 (0.62–2.34)  
  Annual income <50,000 RMB, n = 1,418 Ref 2.15 (1.56–2.97) 1.48 (1.05–2.10)  
LVDD 
 Gender    <0.001 
  Male, n = 1,025 Ref 2.70 (0.87–8.38) 1.66 (0.91–3.04)  
  Female, n = 1,733 Ref 2.04 (0.87–4.80) 1.62 (1.01–2.59)  
 SBP    <0.001 
  SBP ≥140, mm Hg, n = 1,458 Ref 3.25 (2.43–4.33) 2.00 (1.48–2.71)  
  SBP <140, mm Hg, n = 1,300 Ref 1.42 (0.54–3.69) 1.29 (0.77–2.16)  
 Obesity    0.353 
  Yes, n = 475 Ref 0.50 (0.07–3.28) 1.12 (0.42–3.00)  
  No, n = 2,283 Ref 3.02 (2.44–3.74) 1.77 (1.41–2.22)  
 Educational status    0.036 
  High school or above, n = 583 Ref 2.36 (1.52–3.65) 1.69 (1.09–2.62)  
  Below than high school, n = 2,175 Ref 1.74 (0.79–3.80) 1.45 (0.94–2.23)  
 Economic status    0.244 
  Annual income ≥50,000 RMB, n = 1,340 Ref 1.33 (0.49–3.57) 1.29 (0.75–2.21)  
  Annual income <50,000 RMB, n = 1,418 Ref 3.15 (2.38–4.16) 1.84 (1.37–2.48)  

Adjusted for age, gender, educational status, economic status, SBP, DBP, heart rate, body mass index, LDL-C, FBG, stroke, coronary heart disease, hypertension, diabetes.

LVH, left ventricular hypertrophy; LVDD, left ventricular diastolic dysfunction; SBP, systolic blood pressure.

In the present study, we demonstrated that participants exposed to the famine early (fetal or infant stages) exhibited significant abnormalities in their echocardiography, including altered LVH and LVDD, in middle age compared to those born after the famine. The study also found that this effect was more pronounced in females, individuals with high blood pressure, higher education levels, and lower annual incomes. Famine exposure showed no significant effect on serious individuals with LVH, LVDD, and reduced GLS. The incidence of hypertension, obesity, and other risk factors at a later age also contributes to echocardiographic abnormalities.

These results are consistent with an animal study conducted on rats with low-protein exposure who reported that the evaluation of LV function at 40 weeks of age showed a higher occurrence of LVH, normal LVEF, a two-fold increase of LV end-diastolic pressure, and a decrease in diastolic function [27]. From 3 days to 2 weeks of age, the LV wall was observed to be thinner in the low-protein group, followed by a gradual increase [27]. These findings suggest that cardiomyocytes in animals’ hearts that were fed low protein experienced initial eccentric hypertrophy, followed by concentric hypertrophy. In our study, the population exposed to famine showed morphological changes in their hearts, including increased LVEDD/BSA, a higher risk of LVH, reduced diastolic function, and preserved LVEF. Additionally, our results confirmed that famine exposure posed the highest risk for concentric LVH. The initial eccentric hypertrophy is due to a severe loss in cardiomyocytes due to increased apoptosis [27]. The compensatory changes in response to reduced contractility of the heart, such as LV remodeling (LV dilation and LVH), are considered important factors in the preservation of LVEF and the progression of heart failure [28].

In cross-sectional population studies, malnourished children had significantly lower LVM compared to children with normal nutrition between the ages of 2 and 36 months [29, 30]. On the contrary, diastolic function indices and systolic function were not significantly affected by protein energy malnutrition in infants [30, 31]. In a prospective cohort study including 6,239 children, fetal-femur length and weight were measured at birth, 0.5, 1, and 2 years of age. The study also estimated LVM and systolic function at 6 years of age. The results indicated that decelerated growth during fetal life and infancy is associated with a relatively larger LVM [32]. In a previous population study, it was found that children aged 4–5 [17] with severe fetal growth restriction exhibited a larger LV sphericity index and reduced e′. This, in turn, resulted in reduced diastolic function [33]. These observational studies reveal distinct changes in cardiac function in malnourished children of different ages. Early famine exposure was associated with high risk of LVH, LVDD, and increased LVEDD/BSA in middle to old age, which might partly explain the increased risk of CVD described in the epidemiological studies on famine [1, 34].

Interestingly, we found famine exposure showed independent significance in those with slight cardiac damage such as only LVH or LVDD but was not significant in serious individuals with LVH, LVDD, or reduced GLS combination. Changes in traditional risk factors such as hypertension or obesity at a later age may lead to abnormal increases in left ventricular wall stress and dilation. Famine exposure had a stronger effect on LVH and LVDD in individuals with SBP ≥140 mm Hg than SBP <140 mm Hg. Previous study [35] also showed diastolic dysfunction is linked to abnormalities of the cellular mechanisms of myocyte relaxation caused by hypoxia and/or ischemia. Hypertension affects LV relaxation, and when LVH occurs, it decreases compliance too. Traditional risk factors may contribute to worse cardiac remodeling and function. Socioeconomic status also showed interaction with famine exposure. Individuals with high school or above educational level or annual income <50,000 yuan showed higher risk for LVDD or LVH, respectively. Additionally, famine exposure participants showed increased LVMI and LVEDD/BSA compared to the post-famine exposure, but pre-famine exposure did not have an impact on increased LVEDD/BSA. These results suggest that malnutrition during the fetus and infancy stages can lead to enlarged left ventricular and increased LV mass in adults.

The current study confirms and expands on prior research documenting a significantly increased risk of LVH, LVDD, increased LVEDD/BSA, and preserved LVEF in middle-aged or elderly individuals who experienced fetal famine exposure. The echocardiography assessment revealed a greater risk of LVH and LVDD in both the pre-famine and famine exposure groups compared to the post-famine exposure group; however, this effect was more pronounced in the famine exposure group. Research on baboons has revealed that IUGR [36] causes myocardial remodeling, reduces diastolic function, and leads to a premature aging phenotype in the heart. During embryonic development, cardiomyocytes mainly rely on glycolysis and oxidation of lactate for energy supply. In cases of famine exposure, pregnant women may experience a worse energy supply for cardiomyocytes, which could result in poorer cardiac structure and function.

Mitosis in human cardiomyocytes mainly occurs during the first trimester of pregnancy and gradually decreases to very low levels during the late fetal and early postpartum periods [37]. Catch-up growth and increased cell division may cause accelerated telomere shortening, which causes cellular aging in key organs [38], possibly including the heart. Therefore, it is conceivable that cardiac dysfunction during low protein exposure may be due to an early aging effect of the heart. Starvation-induced cardiac changes can also be caused by electrolyte imbalances, hyponatremia, vitamin deficiencies, and anemia alone or in combination.

There are some limitations to the study. First, although we measure the cardiac structure and function in adults, a limitation of our work is that cardiac function measurement was made only at one time and thus the initial deterioration of cardiac changes remained unknown. Although the cardiac ultrasound data acquisition was performed manually by experienced echocardiographers, there are also echocardiographers from Guangdong Provincial People’s Hospital to monitor the data and imaging. This helps overcome the limitation of measurement error. Second, the random sampling method was not used to recruit participants, and it might lead to a high proportion of females. Third, the participants were recruited from southern Guangdong Province. Future studies are needed to investigate the association between famine exposure and cardiac function in the northern provinces or severe famine provinces. Fourth, data on other nutritional evaluation indicators such as albumin, transferrin, and triceps skin fold thickness were not captured. Fifth, the study only included individuals at high risk of CVD, which was not generally representative of other populations at low risk.

We can conclude that famine exposure, especially in fetuses and infancy, increases the risk of LVH and LVDD in adults. However, it appears that LV systolic function remains unaffected. The study also highlights the importance of establishing an appropriate nutrition program for pregnant women as early as possible and the need for more rigorous treatment and ongoing monitoring of malnourished infants and children. These findings provide important information for efforts to prevent and manage the consequences of famine exposure in the human population.

We gratefully acknowledge the contribution of all participants in the present project.

The research protocol was approved by the Central Ethics Committee at the China National Center for Cardiovascular Disease and the Ethics Committee of Guangdong Provincial People’s Hospital (No. GDREC2016438H [R2]). Written informed consent was obtained for all participants before the study. The research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki.

This manuscript has not been published previously. The authors have no conflicts of interest to declare.

This work was supported by the Ministry of Finance of China and the National Health and Family Planning Commission of China, the Key Area R&D Program of Guangdong Province (No. 2019B020227005), the Climbing Plan of Guangdong Provincial People’s Hospital (DFJH2020022), the Guangdong Provincial Clinical Research Center for Cardiovascular Disease (2020B1111170011), and the Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention (2017B030314041).

D.Z., X.F., S.W., and M.Y. conceived the study and drafted the manuscript. J.W. supervised and managed the project. D.Z., X.F., M.Y., and Z.N. analyzed the data. D.Z. and Y.F. critically reviewed the manuscript. All the authors read and approved the final manuscript.

Additional Information

Dan Zhou and Xiaoxuan Feng contributed equally to the article.

Data are not publicly available due to ethical reasons. Further inquiries can be directed to the corresponding author.

1.
Chen
C
,
Nie
Z
,
Wang
J
,
Ou
Y
,
Cai
A
,
Huang
Y
et al
.
Prenatal exposure to the Chinese famine of 1959-62 and risk of cardiovascular diseases in adulthood: findings from the China PEACE million persons project
.
Eur J Prev Cardiol
.
2022
;
29
(
16
):
2111
9
.
2.
Wang
Y
,
Jin
J
,
Peng
Y
,
Chen
Y
.
Exposure to Chinese famine in the early life, adulthood obesity patterns, and the incidence of hypertension: a 22-year cohort study
.
Ann Nutr Metab
.
2021
;
77
(
2
):
109
15
.
3.
Li
J
,
Yang
Q
,
An
R
,
Sesso
HD
,
Zhong
VW
,
Chan
KHK
et al
.
Famine and trajectories of body mass index, waist circumference, and blood pressure in two generations: results from the CHNS from 1993–2015
.
Hypertension
.
2022
;
79
(
3
):
518
31
.
4.
Liu
D
,
Yang
J
,
Wang
S
.
Early-life exposure to famine and the risk of general and abdominal obesity in adulthood: a 22-year cohort study
.
Public health
.
2022
;
202
:
113
20
.
5.
Wei
R
,
Wang
W
,
Pan
Q
,
Guo
L
.
Effect of foetal exposure to famine on the risk of nonalcoholic fatty liver disease in adulthood: a systematic review and meta-analysis
.
Dig Liver Dis
.
2023
;
55
(
7
):
848
55
.
6.
Li
C
,
Lumey
LH
.
Early-life exposure to the Chinese famine of 1959-1961 and type 2 diabetes in adulthood: a systematic review and meta-analysis
.
Nutrients
.
2022
;
14
(
14
):
2855
.
7.
Wang
B
,
Cheng
J
,
Wan
H
,
Wang
Y
,
Zhang
W
,
Chen
Y
et al
.
Early-life exposure to the Chinese famine, genetic susceptibility and the risk of type 2 diabetes in adulthood
.
Diabetologia
.
2021
;
64
(
8
):
1766
74
.
8.
Yao
F
,
Zhao
L
,
Yang
Y
,
Piao
W
,
Fang
H
,
Ju
L
et al
.
Association between early life famine exposure and metabolic syndrome in adulthood
.
Nutrients
.
2022
;
14
(
14
):
2881
.
9.
Hietalampi
H
,
Pahkala
K
,
Jokinen
E
,
Ronnemaa
T
,
Viikari
JS
,
Niinikoski
H
et al
.
Left ventricular mass and geometry in adolescence: early childhood determinants
.
Hypertension
.
2012
;
60
(
5
):
1266
72
.
10.
Barker
DJP
.
Fetal origins of cardiovascular disease
.
Ann Med
.
1999
31
sup1
3
6
.
11.
Osmond
C
,
Barker
DJ
.
Fetal, infant, and childhood growth are predictors of coronary heart disease, diabetes, and hypertension in adult men and women
.
Environ Health Perspect
.
2000
108
Suppl 3
545
53
.
12.
Thornburg
KL
.
The programming of cardiovascular disease
.
J Dev Orig Health Dis
.
2015
;
6
(
5
):
366
76
.
13.
Mollova
M
,
Bersell
K
,
Walsh
S
,
Savla
J
,
Das
LT
,
Park
SY
et al
.
Cardiomyocyte proliferation contributes to heart growth in young humans
.
Proc Natl Acad Sci U S A
.
2013
;
110
(
4
):
1446
51
.
14.
Hamaguchi
S
,
Kawakami
Y
,
Honda
Y
,
Nemoto
K
,
Sano
A
,
Namekata
I
et al
.
Developmental changes in excitation-contraction mechanisms of the mouse ventricular myocardium as revealed by functional and confocal imaging analyses
.
J Pharmacol Sci
.
2013
;
123
(
2
):
167
75
.
15.
Chen
X
,
Wilson
RM
,
Kubo
H
,
Berretta
RM
,
Harris
DM
,
Zhang
X
et al
.
Adolescent feline heart contains a population of small, proliferative ventricular myocytes with immature physiological properties
.
Circ Res
.
2007
;
100
(
4
):
536
44
.
16.
Pérez-Cruz
M
,
Cruz-Lemini
M
,
Fernández
MT
,
Parra
JA
,
Bartrons
J
,
Gómez-Roig
MD
et al
.
Fetal cardiac function in late-onset intrauterine growth restriction vs small-for-gestational age, as defined by estimated fetal weight, cerebroplacental ratio and uterine artery Doppler
.
Ultrasound Obstet Gynecol
.
2015
;
46
(
4
):
465
71
.
17.
Crispi
F
,
Bijnens
B
,
Figueras
F
,
Bartrons
J
,
Eixarch
E
,
Le Noble
F
et al
.
Fetal growth restriction results in remodeled and less efficient hearts in children
.
Circulation
.
2010
;
121
(
22
):
2427
36
.
18.
Tennant
IA
,
Barnett
AT
,
Thompson
DS
,
Kips
J
,
Boyne
MS
,
Chung
EE
et al
.
Impaired cardiovascular structure and function in adult survivors of severe acute malnutrition
.
Hypertension
.
2014
;
64
(
3
):
664
71
.
19.
Lu
J
,
Xuan
S
,
Downing
NS
,
Wu
C
,
Li
L
,
Krumholz
HM
et al
.
Protocol for the China PEACE (Patient-centered evaluative assessment of cardiac Events) million persons project pilot
.
BMJ Open
.
2016
;
6
(
1
):
e010200
.
20.
Li
C
,
Lumey
LH
.
Exposure to the Chinese famine of 1959-61 in early life and long-term health conditions: a systematic review and meta-analysis
.
Int J Epidemiol
.
2017
;
46
(
4
):
1157
70
.
21.
WHO Expert Consultation
.
Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies
.
Lancet
.
2004
;
363
(
9403
):
157
63
.
22.
Lu
J
,
Lu
Y
,
Wang
X
,
Li
X
,
Linderman
GC
,
Wu
C
et al
.
Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project)
.
Lancet
.
2017
;
390
(
10112
):
2549
58
.
23.
Yang
W
,
Lu
J
,
Weng
J
,
Jia
W
,
Ji
L
,
Xiao
J
et al
.
Prevalence of diabetes among men and women in China
.
N Engl J Med
.
2010
;
362
(
12
):
1090
101
.
24.
Stanton
T
,
Leano
R
,
Marwick
TH
.
Prediction of all-cause mortality from global longitudinal speckle strain: comparison with ejection fraction and wall motion scoring
.
Circ Cardiovasc Imaging
.
2009
;
2
(
5
):
356
64
.
25.
Nagueh
SF
,
Appleton
CP
,
Gillebert
TC
,
Marino
PN
,
Oh
JK
,
Smiseth
OA
et al
.
Recommendations for the evaluation of left ventricular diastolic function by echocardiography
.
J Am Soc Echocardiogr
.
2009
;
22
(
2
):
107
33
.
26.
Williams
B
,
Mancia
G
,
Spiering
W
,
Agabiti Rosei
E
,
Azizi
M
,
Burnier
M
et al
.
2018 ESC/ESH Guidelines for the management of arterial hypertension
.
Eur Heart J
.
2018
;
39
(
33
):
3021
104
.
27.
Cheema
KK
,
Dent
MR
,
Saini
HK
,
Aroutiounova
N
,
Tappia
PS
.
Prenatal exposure to maternal undernutrition induces adult cardiac dysfunction
.
Br J Nutr
.
2005
;
93
(
4
):
471
7
.
28.
Sabbah
HN
.
The cardiac support device and the myosplint: treating heart failure by targeting left ventricular size and shape
.
Ann Thorac Surg
.
2003
75
6 Suppl
S13
9
.
29.
El-Sayed
HL
,
Nassar
MF
,
Habib
NM
,
Elmasry
OA
,
Gomaa
SM
.
Structural and functional affection of the heart in protein energy malnutrition patients on admission and after nutritional recovery
.
Eur J Clin Nutr
.
2006
;
60
(
4
):
502
10
.
30.
Ocal
B
,
Unal
S
,
Zorlu
P
,
Tezic
HT
,
Oğuz
D
.
Echocardiographic evaluation of cardiac functions and left ventricular mass in children with malnutrition
.
J Paediatr Child Health
.
2001
;
37
(
1
):
14
7
.
31.
Brent
B
,
Obonyo
N
,
Akech
S
,
Shebbe
M
,
Mpoya
A
,
Mturi
N
et al
.
Assessment of myocardial function in Kenyan children with severe, acute malnutrition: the cardiac physiology in malnutrition (CAPMAL) study
.
JAMA Netw Open
.
2019
;
2
(
3
):
e191054
.
32.
Toemen
L
,
de Jonge
LL
,
Gishti
O
,
van Osch-Gevers
L
,
Taal
HR
,
Steegers
EA
et al
.
Longitudinal growth during fetal life and infancy and cardiovascular outcomes at school-age
.
J Hypertens
.
2016
;
34
(
7
):
1396
406
.
33.
Notomi
Y
,
Popovic
ZB
,
Yamada
H
,
Wallick
DW
,
Martin
MG
,
Oryszak
SJ
et al
.
Ventricular untwisting: a temporal link between left ventricular relaxation and suction
.
Am J Physiol Heart Circ Physiol
.
2008
294
1
H505
13
.
34.
Chen
CL
,
Wang
JB
,
Huang
YQ
,
Feng
YQ
.
Association between famine exposure in early life and risk of hospitalization for heart failure in adulthood
.
Front Public Health
.
2022
;
10
:
973753
.
35.
Palmiero
P
,
Zito
A
,
Maiello
M
,
Cameli
M
,
Modesti
PA
,
Muiesan
ML
et al
.
Left ventricular diastolic function in hypertension: methodological considerations and clinical implications
.
J Clin Med Res
.
2015
;
7
(
3
):
137
44
.
36.
Kuo
AH
,
Li
C
,
Li
J
,
Huber
HF
,
Nathanielsz
PW
,
Clarke
GD
.
Cardiac remodelling in a baboon model of intrauterine growth restriction mimics accelerated ageing
.
J Physiol
.
2017
;
595
(
4
):
1093
110
.
37.
Erokhina
IL
,
Selivanova
GV
,
Vlasova
TD
,
Emel’ianova
OI
,
Lagutenko
OI
.
[Mitotic activity, ploidy and ultrastructure of cardiomyocytes from human embryo and fetuses]
.
Tsitologiia
.
2000
;
42
(
2
):
146
53
.
38.
Jennings
BJ
,
Ozanne
SE
,
Dorling
MW
,
Hales
CN
.
Early growth determines longevity in male rats and may be related to telomere shortening in the kidney
.
FEBS Lett
.
1999
;
448
(
1
):
4
8
.