Introduction: Renal function has an important bearing on plasma homocysteine levels. Plasma homocysteine is related to left ventricular hypertrophy (LVH). However, it remains unclear whether the association between plasma homocysteine levels and LVH is influenced by renal function. This study aimed to investigate relationships among left ventricular mass index (LVMI), plasma homocysteine levels, and renal function in a population from southern China. Methods: A cross-sectional study was performed in 2,464 patients from June 2016 to July 2021. Patients were divided into three groups based on gender-specific tertiles of homocysteine levels. LVMI ≥115 g/m2 for man or ≥95 g/m2 for woman was defined as LVH. Results: LVMI and the percentage of LVH were increased, while estimated glomerular filtration rate (eGFR) was decreased with the increase in homocysteine levels, both significantly. Multivariate stepwise regression analysis showed that eGFR and homocysteine were independently associated with LVMI in patients with hypertension. No correlation was observed between homocysteine and LVMI in patients without hypertension. Stratified by eGFR, further analysis confirmed homocysteine was independently associated with LVMI (β = 0.126, t = 4.333, p < 0.001) only in hypertensive patients with eGFR ≥90 mL/(min·1.73 m2), not with 60≤ eGFR <90 mL/(min·1.73 m2). Multivariate logistic regression indicated that in hypertensive patients with eGFR ≥90 mL/(min·1.73 m2), the patients in high tertile of homocysteine levels had a nearly twofold increased risk of occurring LVH compared with those in low tertile (high tertile: OR = 2.780, 95% CI: 1.945–3.975, p < 0.001). Conclusion: Plasma homocysteine levels were independently associated with LVMI in hypertensive patients with normal eGFR.

There is a close causal relationship between hypertension and cardiovascular morbidity and mortality. As an important target organ damage in hypertension, left ventricular hypertrophy (LVH), often defined by increased left ventricular mass index (LVMI), predicts heart failure and sudden death with poor prognosis, and is a major independent risk factor for cardiovascular morbidity and mortality [1].

Homocysteine (Hcy) is a sulfur-containing amino acid and product of demethylation of methionine. Plasma Hcy is positively correlated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) [2], and patients with hyperhomocysteinemia (HHcy) are at high risk of hypertension, stroke, and cardiovascular disease [3, 4]. The Framingham Heart Study demonstrated that plasma Hcy was directly related to left ventricular mass (LVM) and wall thickness [5]. In addition, one recent study confirmed that HHcy was significantly and independently associated with LVH [6]. Levels of plasma Hcy mainly depends on the rate of Hcy formation (transmethylations), as well as on the rate of its removal [7]. The kidney plays a key role in the metabolism and clearance of Hcy, and plasma Hcy levels are negatively correlated with glomerular filtration rate (GFR) [8]. However, it remains unclear whether the association between plasma Hcy levels and LVH is influenced by renal function. This study aimed to investigate relationships among LVMI, plasma Hcy, and renal function, defined by estimated GFR (eGFR), in a population from southern China.

Patients

This study was a cross-sectional clinical analysis. A specific protocol was established prior to data collection and performed by well-trained physicians. Patients were recruited from outpatients or inpatients admitted to the departments of General Medicine and Geriatrics of the First Affiliated Hospital of Fujian Medical University from June 2016 to July 2021. The criteria for consultation or admission were as follows: routine health examination, hyperlipidemia, hyperuricemia, gout, carotid plaques, dizziness, peptic ulcer, hypertension, diabetes, coronary heart disease, osteoporosis, etc. A total of 2,829 patients were enrolled in this study, with 365 patients excluded according to the following exclusion criteria: (1) younger than 18 years old; (2) secondary hypertension; (3) acute cardiovascular and cerebrovascular events in recent 6 months; (4) myocardiopathy; (5) valvular heart diseases; (6) severe arrhythmia; (7) chronic liver failure; (8) eGFR <60 mL/(min·1.73 m2); (9) active malignancy; (10) acute infections; (11) connective tissue diseases; (12) moderate or severe anemia; (13) hypothyroidism; (14) use of folic acid, vitamin B6 and vitamin B12; (15) pregnancy; (16) incomplete data. Finally, 2,464 patients were included for the final analysis.

Clinical Data

General information, and physical and laboratory examination data were collected. Patients were reviewed regarding age, gender, smoking habits, medical history of hypertension and diabetes, and use of medications. Height and body weight were recorded in light clothes without shoes. Height was rounded up to the nearest 0.5 cm and body weight to 0.1 kg. Body mass index (BMI) was calculated as the ratio of the body weight (kg) to the square of height (m2). Current smoking was defined as consuming no less than one cigarette per day for at least 6 months. According to the criteria of 2018 Chinese guidelines for prevention and treatment of hypertension, hypertension was defined as a clinic SBP ≥140 mm Hg and/or DBP ≥90 mm Hg without the use of antihypertensive medications at 3 clinic visits on different days. Subjects with a blood pressure <140/90 mm Hg but having hypertensive history and currently taking antihypertensive medication were also considered hypertensives [9]. Based on the criteria recommended by the Chinese Diabetes Society in 2017 [10], diabetes was defined as taking antihyperglycemic medications or establishing a new diagnosis of diabetes.

All patients were told not to consume tea and coffee or take vigorous activities 2 h before measurement. After resting for at least 5 min in a sitting position, heart rate was recorded, and blood pressure was measured using an automated sphygmomanometer (HBP-1300; Omron). There were continuous measurements for 3 times, with an interval of at least 1 min. The average reading of 3 measurements was used for subsequent analysis. The venous blood sample was obtained after 8-h overnight fasting. Levels of fasting plasma glucose, uric acid, total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using an automatic biochemical analyzer (ADVIA 2400; Siemens, Germany). Glycosylated hemoglobin A1c (HbA1c) was detected by high-performance liquid chromatography using an automatic analyzer (VARIANT II; Bio-Rad). Plasma Hcy levels were detected by chemiluminescence microparticle immunoassay (Abbott GmbH & Co. KG, Wiesbaden-Delkenheim, Germany). The intra-variable coefficient of plasma Hcy was 3%, and the inter-variable coefficient was 5%, as our previous research showed [11]. Serum creatinine was measured by Roche enzymatic method (cobas 8000). eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation: eGFR (mL/[min·1.73 m2]) = 141 × min (Scr [mg/dL]/κ, 1)α × max (Scr [mg/dL]/κ, 1)−1.209 × 0.993Age × 1.018 (if female), where κ is 0.7 for females and 0.9 for males, α is −0.329 for females and −0.411 for males, min indicates the minimum of Scr/κ or 1, and max indicates the maximum of Scr/κ or 1 [12].

Transthoracic echocardiography was performed on a GE LOGIQ 7 ultrasound system. Left ventricular end-diastolic internal diameter (LVEDD), interventricular septum thickness, and left ventricular posterior wall thickness were measured using two-dimensional echocardiographic method. All these parameters were determined for five consecutive cardiac cycles, and then, the average value was calculated. The LVM was calculated by the Devereux equation: LVM (g) = 0.8 × 1.04 × ([LVEDD + left ventricular posterior wall thickness + interventricular septum thickness)3 − LVEDD3) + 0.6 [13], body surface area (BSA) by the experimental equation: BSA (m2) = 0.0061 × height (cm) + 0.0128 × body weight (kg) − 0.1529 [14], and LVMI calculated as the ratio of LVM (g) to BSA (m2). LVMI ≥115 g/m2 for man or ≥95 g/m2 for woman was defined as LVH [9].

Statistical Analysis

Continuous variables were expressed as mean ± standard deviation for Gaussian distribution or median (25th–75th) for skewed distribution. Categorical variables were presented as absolute numbers and percentages. Hcy and triglyceride levels were logarithmically (log) transformed for analysis because of their skewed distribution. Comparisons among groups were performed by ANOVA if the continuous variables were in a Gaussian distribution. If the continuous variables were in a skewed distribution, comparisons among groups were made by nonparametric test. Categorical variables were compared by χ2 test. The correlation between variables was assessed by Spearman correlation analysis and stepwise multivariate linear regression. Multivariate logistic regression was performed to identify the association between tertiles of Hcy levels and LVH. Statistical significance was accepted at p < 0.05 for individual data. All statistical analyses were performed using SPSS 20.0 statistical software package.

Clinical Characteristics of all Patients

2,464 patients were included in this study. Clinical and biochemical data of all patients are summarized in Table 1. The mean age of patients was 58.8 ± 11.4 years, 1,574 (63.9%) were male, and 890 (36.1%) were female. Of all patients, 1,695 (68.8%) were diagnosed with hypertension and 719 (29.2%) with diabetes. The plasma Hcy levels ranged from 2.54 to 116.1 μmol/L with a median value of 11.26 μmol/L in male patients and 8.90 μmol/L in female patients. As for the values of tertiles of Hcy levels, ranges were shown as follows: for male patients, low tertile was 4.03–10.20 μmol/L, median tertile 10.21–12.62 μmol/L, and high tertile 12.63–116.1 μmol/L; for female patients, low tertile was 2.54–8.00 μmol/L, median tertile 8.01–10.00 μmol/L, and high tertile 10.01–88.38 μmol/L. Refer to previous research data [15], based on gender; all patients were divided into three groups according to tertiles of Hcy levels: low tertile (n = 826), median tertile (n = 818), and high tertile (n = 820).

Table 1.

Comparison of demographic characteristics of all patients in tertiles of homocysteine levels (n = 2,464)

VariablesTertile 1Tertile 2Tertile 3F2p value
Homocysteine, µmol/L 7.99 (7.20–9.29) 10.67 (9.30–11.56)a 14.00 (12.74–16.29)a, b 1,645.739 <0.001 
Male homocysteine, µmol/L 8.94 (8.14–9.68) 11.27 (10.76–11.96)a 14.92 (13.61–17.37)a, b 1,409.919 <0.001 
Female homocysteine, µmol/L 7.08 (6.30–7.59) 8.90 (8.36–9.46)a 11.75 (10.80–13.58)a, b 914.041 <0.001 
Male, n (%) 529 (64.0) 521 (63.7) 524 (63.9) 0.022 0.989 
Age, years 56.4±10.8 58.3±10.2a 61.9±12.5a, b 50.725 <0.001 
BMI, kg/m2 24.5±3.0 24.9±3.1a 24.7±3.2 3.335 0.036 
Current smoking, n (%) 215 (26.0) 201 (24.6) 194 (23.7) 1.264 0.532 
Hypertension, n (%) 492 (59.6) 576 (70.4)a 627 (76.5)a, b 56.244 <0.001 
Diabetes, n (%) 248 (30.0) 230 (28.1) 241 (29.4) 0.749 0.688 
Systolic blood pressure, mm Hg 128.9±17.8 130.8±17.4 133.4±19.1a, b 13.040 <0.001 
Diastolic blood pressure, mm Hg 79.6±11.6 79.9±11.3 78.9±11.9 1.667 0.189 
Heart rate, bp 71.6±10.6 70.9±11.4 71.8±11.6 1.703 0.182 
Fasting plasma glucose, mmol/L 5.90±1.80 5.68±1.70a 5.59±1.51a 7.846 <0.001 
HbA1c, % 6.11±1.29 6.06±1.23 6.06±1.18 0.432 0.649 
eGFR, mL/(min·1.73 m2101.4±11.4 97.7±11.4a 91.8±12.9a, b 135.145 <0.001 
Creatinine, μmol/L 63.1±13.2 66.2±14.0a 70.2±14.3a, b 54.204 <0.001 
Uric acid, μmol/L 356.1±89.4 371.0±88.7a 385.4±95.9a, b 21.176 <0.001 
Total cholesterol, mmol/L 4.63±1.02 4.71±1.14 4.63±1.08 1.450 0.235 
Triglyceride, mmol/L 1.26 (0.91–1.81) 1.32 (0.95–1.90) 1.31 (0.97–1.86) 1.497 0.224 
HDL-C, mmol/L 1.24±0.33 1.24±0.35 1.24±0.40 0.023 0.997 
LDL-C, mmol/L 2.95±0.93 3.02±1.06 2.99±0.98 1.195 0.303 
LVMI, g/m2 93.8±21.3 97.2±21.6a 101.1±24.5a, b 21.908 <0.001 
Male LVMI, g/m2 97.5±21.9 99.6±22.2 103.0±24.1a, b 7.953 <0.001 
Female LVMI, g/m2 87.1±18.5 92.9±19.7a 97.7±24.8a, b 18.441 <0.001 
Duration of hypertension, years 1.0 (0.0–7.0) 3.0 (0.0–7.0)a 7.0 (0.0–10.0)a, b <0.001 
Use of statin, n (%) 134 (16.2) 171 (20.9)a 169 (20.6)a 7.289 0.026 
Use of ACEI/ARB, n (%) 200 (24.2) 257 (31.4)a 268 (32.7)a 16.561 <0.001 
Use of β blocker, n (%) 88 (10.7) 113 (13.8) 120 (14.6)a 6.423 0.040 
Use of CCB, n (%) 202 (24.5) 226 (27.6) 299 (36.5)a, b 30.601 <0.001 
Use of diuretic, n (%) 35 (4.2) 40 (4.9) 55 (6.7) 5.389 0.068 
VariablesTertile 1Tertile 2Tertile 3F2p value
Homocysteine, µmol/L 7.99 (7.20–9.29) 10.67 (9.30–11.56)a 14.00 (12.74–16.29)a, b 1,645.739 <0.001 
Male homocysteine, µmol/L 8.94 (8.14–9.68) 11.27 (10.76–11.96)a 14.92 (13.61–17.37)a, b 1,409.919 <0.001 
Female homocysteine, µmol/L 7.08 (6.30–7.59) 8.90 (8.36–9.46)a 11.75 (10.80–13.58)a, b 914.041 <0.001 
Male, n (%) 529 (64.0) 521 (63.7) 524 (63.9) 0.022 0.989 
Age, years 56.4±10.8 58.3±10.2a 61.9±12.5a, b 50.725 <0.001 
BMI, kg/m2 24.5±3.0 24.9±3.1a 24.7±3.2 3.335 0.036 
Current smoking, n (%) 215 (26.0) 201 (24.6) 194 (23.7) 1.264 0.532 
Hypertension, n (%) 492 (59.6) 576 (70.4)a 627 (76.5)a, b 56.244 <0.001 
Diabetes, n (%) 248 (30.0) 230 (28.1) 241 (29.4) 0.749 0.688 
Systolic blood pressure, mm Hg 128.9±17.8 130.8±17.4 133.4±19.1a, b 13.040 <0.001 
Diastolic blood pressure, mm Hg 79.6±11.6 79.9±11.3 78.9±11.9 1.667 0.189 
Heart rate, bp 71.6±10.6 70.9±11.4 71.8±11.6 1.703 0.182 
Fasting plasma glucose, mmol/L 5.90±1.80 5.68±1.70a 5.59±1.51a 7.846 <0.001 
HbA1c, % 6.11±1.29 6.06±1.23 6.06±1.18 0.432 0.649 
eGFR, mL/(min·1.73 m2101.4±11.4 97.7±11.4a 91.8±12.9a, b 135.145 <0.001 
Creatinine, μmol/L 63.1±13.2 66.2±14.0a 70.2±14.3a, b 54.204 <0.001 
Uric acid, μmol/L 356.1±89.4 371.0±88.7a 385.4±95.9a, b 21.176 <0.001 
Total cholesterol, mmol/L 4.63±1.02 4.71±1.14 4.63±1.08 1.450 0.235 
Triglyceride, mmol/L 1.26 (0.91–1.81) 1.32 (0.95–1.90) 1.31 (0.97–1.86) 1.497 0.224 
HDL-C, mmol/L 1.24±0.33 1.24±0.35 1.24±0.40 0.023 0.997 
LDL-C, mmol/L 2.95±0.93 3.02±1.06 2.99±0.98 1.195 0.303 
LVMI, g/m2 93.8±21.3 97.2±21.6a 101.1±24.5a, b 21.908 <0.001 
Male LVMI, g/m2 97.5±21.9 99.6±22.2 103.0±24.1a, b 7.953 <0.001 
Female LVMI, g/m2 87.1±18.5 92.9±19.7a 97.7±24.8a, b 18.441 <0.001 
Duration of hypertension, years 1.0 (0.0–7.0) 3.0 (0.0–7.0)a 7.0 (0.0–10.0)a, b <0.001 
Use of statin, n (%) 134 (16.2) 171 (20.9)a 169 (20.6)a 7.289 0.026 
Use of ACEI/ARB, n (%) 200 (24.2) 257 (31.4)a 268 (32.7)a 16.561 <0.001 
Use of β blocker, n (%) 88 (10.7) 113 (13.8) 120 (14.6)a 6.423 0.040 
Use of CCB, n (%) 202 (24.5) 226 (27.6) 299 (36.5)a, b 30.601 <0.001 
Use of diuretic, n (%) 35 (4.2) 40 (4.9) 55 (6.7) 5.389 0.068 

Data were expressed as means ± SD or median (25th–75th).

ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, Ca2+ channel blocker; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVMI, left ventricular mass index.

ap < 0.05 versus tertile 1 group.

bp < 0.05 versus tertile 2 group.

Age, percentage of patients with hypertension, duration of hypertension, SBP, serum creatinine, and uric acid were increased with the ascending tertiles of Hcy (p < 0.001). LVMI was increased ([93.8 ± 21.3], [97.2 ± 21.6], [101.1 ± 24.5] g/m2; p < 0.001), while eGFR decreased ([101.4 ± 11.4], [97.7 ± 11.4], [91.8 ± 12.9] mL/[min·1.73 m2]; p < 0.001) with the ascending tertiles of Hcy. There were no significant differences in percentages of patients with diabetes, DBP, heart rate, HbA1c, total cholesterol, triglyceride, HDL-C, and LDL-C among tertiles of Hcy (shown in Table 1).

Percentages of LVH in tertiles of Hcy are shown in Figure 1. Percentages of LVH were distinctly increased with the rising tertiles of Hcy (20.8%, 28.0%, 35.6%, χ2trend = 44.486, p < 0.001) (shown in Fig. 1).

Fig. 1.

Percentages of LVH in tertiles of homocysteine levels (n = 2,464). LVH, left ventricular hypertrophy.

Fig. 1.

Percentages of LVH in tertiles of homocysteine levels (n = 2,464). LVH, left ventricular hypertrophy.

Close modal

Spearman Correlation Analysis between LVMI and Clinical Profile

Spearman correlation analysis showed that Hcy was positively correlated with LVMI both in non-hypertensive patients (r = 0.170, p < 0.001) and in hypertensives (r = 0.145, p < 0.001). Meanwhile, age, current smoking, diabetes, BMI, SBP, uric acid, and HbA1c were positively related to LVMI in both groups. In addition, gender (male = 1, female = 2), heart rate, eGFR, and HDL-C were negatively related to LVMI in both groups (shown in Table 2).

Table 2.

Correlation of clinical profile and LVMI in non-hypertensives and hypertensives

VariablesNon-hypertensives (n = 769)Hypertensives (n = 1,695)
rp valuerp value
Age 0.159 <0.001 0.195 <0.001 
Gender (male = 1, female = 2) −0.213 <0.001 −0.127 <0.001 
Current smoking (N = 0, Y = 1) 0.108 0.003 0.142 <0.001 
Diabetes (N = 0, Y = 1) 0.081 0.025 0.076 0.002 
BMI 0.266 <0.001 0.116 <0.001 
Systolic blood pressure 0.213 <0.001 0.165 <0.001 
Diastolic blood pressure 0.137 <0.001 −0.061 0.012 
Heart rate −0.171 <0.001 −0.211 <0.001 
eGFR −0.102 0.005 −0.095 <0.001 
Creatinine 0.129 <0.001 0.037 0.128 
Uric acid 0.136 <0.001 0.080 0.001 
Total cholesterol −0.083 0.021 −0.130 <0.001 
Triglyceride 0.087 0.016 −0.031 0.195 
HDL-C −0.097 0.007 −0.116 <0.001 
LDL-C −0.059 0.100 −0.090 <0.001 
Fasting plasma glucose 0.067 0.064 0.001 0.982 
HbA1c 0.119 0.001 0.087 <0.001 
Homocysteine 0.170 <0.001 0.145 <0.001 
Duration of hypertension 0.170 <0.001 
Use of statin 0.029 0.420 0.023 0.345 
Use of ACEI/ARB 0.099 <0.001 
Use of β blocker 0.019 0.598 0.060 0.013 
Use of Ca2+ channel blocker 0.097 <0.001 
Use of diuretic 0.064 0.008 
VariablesNon-hypertensives (n = 769)Hypertensives (n = 1,695)
rp valuerp value
Age 0.159 <0.001 0.195 <0.001 
Gender (male = 1, female = 2) −0.213 <0.001 −0.127 <0.001 
Current smoking (N = 0, Y = 1) 0.108 0.003 0.142 <0.001 
Diabetes (N = 0, Y = 1) 0.081 0.025 0.076 0.002 
BMI 0.266 <0.001 0.116 <0.001 
Systolic blood pressure 0.213 <0.001 0.165 <0.001 
Diastolic blood pressure 0.137 <0.001 −0.061 0.012 
Heart rate −0.171 <0.001 −0.211 <0.001 
eGFR −0.102 0.005 −0.095 <0.001 
Creatinine 0.129 <0.001 0.037 0.128 
Uric acid 0.136 <0.001 0.080 0.001 
Total cholesterol −0.083 0.021 −0.130 <0.001 
Triglyceride 0.087 0.016 −0.031 0.195 
HDL-C −0.097 0.007 −0.116 <0.001 
LDL-C −0.059 0.100 −0.090 <0.001 
Fasting plasma glucose 0.067 0.064 0.001 0.982 
HbA1c 0.119 0.001 0.087 <0.001 
Homocysteine 0.170 <0.001 0.145 <0.001 
Duration of hypertension 0.170 <0.001 
Use of statin 0.029 0.420 0.023 0.345 
Use of ACEI/ARB 0.099 <0.001 
Use of β blocker 0.019 0.598 0.060 0.013 
Use of Ca2+ channel blocker 0.097 <0.001 
Use of diuretic 0.064 0.008 

ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Stepwise Multivariate Linear Regression Analysis for LVMI

In patients without hypertension, multiple stepwise regression analysis showed that BMI and age were independently associated with LVMI regardless of eGFR levels, while Hcy was not associated with LVMI (shown in Table 3). In patients with hypertension, multiple stepwise regression analysis showed that eGFR levels and Lg Hcy were independently associated with LVMI. Stratified by eGFR, further analysis showed Lg Hcy was independently associated with LVMI (β = 0.126, t = 4.333, p < 0.001) in patients with eGFR ≥90 mL/(min·1.73 m2), even after adjusting for age, gender, current smoking, diabetes, BMI, SBP, DBP, heart rate, eGFR, creatinine, uric acid, total cholesterol, Lg triglyceride, HDL-C, LDL-C, fasting plasma glucose, HbA1c, duration of hypertension, use of statin, and antihypertensive agents, including angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), β receptor blocker, Ca2+ channel blocker, and diuretic. However, no relationship was observed between the levels of Hcy and LVMI in hypertensive patients with 60≤ eGFR <90 mL/(min·1.73 m2) (shown in Table 4).

Table 3.

Stepwise multivariate linear regression analysis for LVMI before and after stratification of eGFR in patients without hypertension

GroupNVariablesBSEβtp value
Non-hypertensives 769 BMI 1.288 0.216 0.206 5.972 <0.001 
Age 0.426 0.079 0.239 5.365 <0.001 
Heart rate −0.304 0.054 −0.186 −5.664 <0.001 
SBP 0.213 0.054 0.135 3.972 <0.001 
Gender −5.715 1.179 −0.164 −4.848 <0.001 
eGFR 0.138 0.068 0.090 2.015 0.044 
eGFR ≥90 mL/(min·1.73 m2660 BMI 1.227 0.228 0.197 5.370 <0.001 
Age 0.297 0.067 0.158 4.443 <0.001 
Heart rate −0.343 0.057 −0.211 −6.058 <0.001 
SBP 0.247 0.057 0.159 4.339 <0.001 
Gender −5.571 1.228 −0.163 −4.536 <0.001 
60≤ eGFR <90 mL/(min·1.73 m2109 Age 0.692 0.176 0.360 3.930 <0.001 
BMI 1.596 0.596 0.245 2.676 0.009 
GroupNVariablesBSEβtp value
Non-hypertensives 769 BMI 1.288 0.216 0.206 5.972 <0.001 
Age 0.426 0.079 0.239 5.365 <0.001 
Heart rate −0.304 0.054 −0.186 −5.664 <0.001 
SBP 0.213 0.054 0.135 3.972 <0.001 
Gender −5.715 1.179 −0.164 −4.848 <0.001 
eGFR 0.138 0.068 0.090 2.015 0.044 
eGFR ≥90 mL/(min·1.73 m2660 BMI 1.227 0.228 0.197 5.370 <0.001 
Age 0.297 0.067 0.158 4.443 <0.001 
Heart rate −0.343 0.057 −0.211 −6.058 <0.001 
SBP 0.247 0.057 0.159 4.339 <0.001 
Gender −5.571 1.228 −0.163 −4.536 <0.001 
60≤ eGFR <90 mL/(min·1.73 m2109 Age 0.692 0.176 0.360 3.930 <0.001 
BMI 1.596 0.596 0.245 2.676 0.009 

Gender: male = 1, female = 2.

eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure.

Candidate variables: age, gender (male = 1, female = 2), current smoking (no = 0, yes = 1), diabetes (no = 0, yes = 1), BMI, SBP, diastolic blood pressure, heart rate, eGFR, creatinine, uric acid, total cholesterol, Lg triglyceride, HDL-C, LDL-C, fasting plasma glucose, HbA1c, Lg homocysteine, use of statin, and β blocker (no = 0, yes = 1).

Table 4.

Stepwise multivariate linear regression analysis for LVMI before and after stratification of eGFR in patients with hypertension

GroupNVariablesBSEβtp value
Hypertensives 1,695 Heart rate −0.337 0.048 −0.166 −7.073 <0.001 
SBP 0.323 0.041 0.238 7.868 <0.001 
Gender −9.617 1.914 −0.195 −5.024 <0.001 
DBP −0.218 0.069 −0.111 −3.143 0.002 
Duration of hypertension 0.304 0.084 0.088 3.601 <0.001 
Smoking 5.107 1.327 0.095 3.849 <0.001 
BMI 0.497 0.175 0.067 2.841 0.005 
Creatinine −0.332 0.087 −0.205 −3.798 <0.001 
Lg homocysteine 13.718 3.916 0.087 3.503 <0.001 
HDL-C −3.934 1.575 −0.060 −2.498 0.013 
eGFR −0.193 0.087 −0.105 −2.202 0.028 
eGFR ≥90 mL/(min·1.73 m21,172 Heart rate −0.386 0.053 −0.196 −7.328 <0.001 
SBP 0.306 0.037 0.222 8.239 <0.001 
Gender −8.499 1.922 −0.179 −4.422 <0.001 
Creatinine −0.350 0.069 −0.188 −5.072 <0.001 
Duration of hypertension 0.354 0.105 0.090 3.364 0.001 
BMI 0.784 0.193 0.110 4.072 <0.001 
Lg homocysteine 19.707 4.548 0.126 4.333 <0.001 
Total cholesterol −2.250 0.568 −0.109 −3.962 <0.001 
Smoking 5.794 1.497 0.113 3.870 <0.001 
60≤ eGFR <90 mL/(min·1.73 m2523 Heart rate −0.277 0.094 −0.127 −2.936 0.003 
Smoking 6.188 2.463 0.107 2.513 0.012 
Uric acid 0.028 0.012 0.104 2.396 0.017 
SBP 0.318 0.066 0.241 4.831 <0.001 
DBP −0.510 0.109 −0.241 −4.701 <0.001 
Duration of hypertension 0.293 0.131 0.096 2.247 0.025 
GroupNVariablesBSEβtp value
Hypertensives 1,695 Heart rate −0.337 0.048 −0.166 −7.073 <0.001 
SBP 0.323 0.041 0.238 7.868 <0.001 
Gender −9.617 1.914 −0.195 −5.024 <0.001 
DBP −0.218 0.069 −0.111 −3.143 0.002 
Duration of hypertension 0.304 0.084 0.088 3.601 <0.001 
Smoking 5.107 1.327 0.095 3.849 <0.001 
BMI 0.497 0.175 0.067 2.841 0.005 
Creatinine −0.332 0.087 −0.205 −3.798 <0.001 
Lg homocysteine 13.718 3.916 0.087 3.503 <0.001 
HDL-C −3.934 1.575 −0.060 −2.498 0.013 
eGFR −0.193 0.087 −0.105 −2.202 0.028 
eGFR ≥90 mL/(min·1.73 m21,172 Heart rate −0.386 0.053 −0.196 −7.328 <0.001 
SBP 0.306 0.037 0.222 8.239 <0.001 
Gender −8.499 1.922 −0.179 −4.422 <0.001 
Creatinine −0.350 0.069 −0.188 −5.072 <0.001 
Duration of hypertension 0.354 0.105 0.090 3.364 0.001 
BMI 0.784 0.193 0.110 4.072 <0.001 
Lg homocysteine 19.707 4.548 0.126 4.333 <0.001 
Total cholesterol −2.250 0.568 −0.109 −3.962 <0.001 
Smoking 5.794 1.497 0.113 3.870 <0.001 
60≤ eGFR <90 mL/(min·1.73 m2523 Heart rate −0.277 0.094 −0.127 −2.936 0.003 
Smoking 6.188 2.463 0.107 2.513 0.012 
Uric acid 0.028 0.012 0.104 2.396 0.017 
SBP 0.318 0.066 0.241 4.831 <0.001 
DBP −0.510 0.109 −0.241 −4.701 <0.001 
Duration of hypertension 0.293 0.131 0.096 2.247 0.025 

Gender: male = 1, female = 2.

DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure.

Candidate variables: age, gender (male = 1, female = 2), current smoking (no = 0, yes = 1), diabetes (no = 0, yes = 1), BMI, SBP, DBP, heart rate, eGFR, creatinine, uric acid, total cholesterol, Lg triglyceride, HDL-C, LDL-C, fasting plasma glucose, HbA1c, Lg homocysteine, duration of hypertension, use of statin, and antihypertensive agents (no = 0, yes = 1).

Multivariate Logistic Regression Analysis for LVH before and after Stratification of eGFR

In patients with hypertension, increasing odds ratios (ORs) for LVH were displayed from the low tertile to high tertile of Hcy levels. The patients in high tertile of Hcy had 2.1-fold risk of LVH compared with those in low tertile (high tertile: OR = 2.100, 95% CI: 1.566–2.814, p < 0.001) (shown in Table 5).

Table 5.

ORs of tertiles of homocysteine levels for LVH in multivariate logistic regression analysis

GroupNHomocysteineBSEWald χ2p valueOR95% CI
Hypertensives 1,695 Tertile 1 1.000 Reference 
Tertile 2 0.344 0.148 5.432 0.020 1.411 1.056–1.884 
Tertile 3 0.742 0.149 24.627 <0.001 2.100 1.566–2.814 
eGFR ≥90 mL/(min·1.73 m21,172 Tertile 1 1.000 Reference 
Tertile 2 0.586 0.169 12.031 0.001 1.797 1.290–2.503 
Tertile 3 1.022 0.182 31.434 <0.001 2.780 1.945–3.975 
GroupNHomocysteineBSEWald χ2p valueOR95% CI
Hypertensives 1,695 Tertile 1 1.000 Reference 
Tertile 2 0.344 0.148 5.432 0.020 1.411 1.056–1.884 
Tertile 3 0.742 0.149 24.627 <0.001 2.100 1.566–2.814 
eGFR ≥90 mL/(min·1.73 m21,172 Tertile 1 1.000 Reference 
Tertile 2 0.586 0.169 12.031 0.001 1.797 1.290–2.503 
Tertile 3 1.022 0.182 31.434 <0.001 2.780 1.945–3.975 

eGFR, estimated glomerular filtration rate; OR, odds ratio.

Multivariate: adjustment for age, current smoking, diabetes, BMI, systolic blood pressure, diastolic blood pressure, heart rate, eGFR, creatinine, uric acid, total cholesterol, Lg triglyceride, HDL-C, LDL-C, fasting plasma glucose, HbA1c, duration of hypertension, and use of medication.

Stratified by eGFR, in hypertensive patients with eGFR ≥90 mL/(min·1.73 m2), further analysis indicated that the patients in high tertile of Hcy showed a nearly twofold increased risk of LVH compared with those in low tertile (high tertile: OR = 2.780, 95% CI: 1.945–3.975, p < 0.001). However, in hypertensive patients with eGFR <90 mL/(min·1.73 m2), there was no significant association between Hcy levels and LVH (shown in Table 5).

In this study, the relationships among LVMI, plasma Hcy levels, and eGFR were analyzed in 769 patients without hypertension and 1,695 patients with hypertension. The results showed that blood pressure levels and LVMI increased, while eGFR levels decreased with the increase in plasma Hcy levels. And Hcy levels were positively correlated with LVMI. Further analysis indicated that, after adjusting for other cardiovascular risk factors, plasma Hcy levels were independently correlated with LVMI, and high levels of Hcy were independently correlated with a higher risk of LVH only in patients with hypertension, not with non-hypertension. Furthermore, stratified analysis based on eGFR showed that this independent association was found only in hypertensive patients with eGFR ≥90 mL/(min·1.73 m2), not with reduced eGFR.

Hcy is an intermediate metabolite of methionine and a toxic amino acid containing sulfhydryl groups. A variety of factors can lead to the accumulation of Hcy concentration in blood, including genetic deficiency of metabolic enzymes, insufficient intake or excessive consumption of folic acid, vitamin B6, and vitamin B12 due to diseases or unhealthy lifestyle, such as renal dysfunction and hypothyroidism, severe anemia and malignant tumor, and use of medications such as methotrexate [16‒18]. In recent years, studies have revealed the harmful effect of HHcy in many adverse health conditions. Our study showed that age, percentage of patients with hypertension, SBP, serum creatinine, uric acid, and LVMI were significantly increased along with Hcy levels increasing. These findings were consistent with previous studies [2, 11]. High levels of Hcy have been considered an independent risk factor for hypertension, stroke, ischemic heart disease, progress of CKD, and diabetic macro- and microvascular complications [2‒4, 19‒21]. Our previous study also showed a close association between Hcy and carotid atherosclerosis in hypertension [22]. Furthermore, some studies demonstrated that levels of Hcy were independently associated with LVMI [23, 24], and elevated levels of Hcy were correlated with LVH [6, 25]. This study indicated that high levels of Hcy correlated with a higher risk of LVH in hypertension. However, the underlying pathogenesis remains unclear. Studies have shown that multiple pathophysiologic mechanisms may be involved in Hcy-mediated cardiac hypertrophy, including the increase in oxidative stress and density of mast cells in the heart [26, 27], and the upregulation of endoplasmic reticulum stress [28]. Clinically, for the prevention of cardiovascular and cerebrovascular diseases, it is necessary to emphasize early comprehensive intervention of risk factors, not only focusing on hypertension, hyperglycemia, and hyperlipidemia, but also paying attention to high levels of Hcy. A randomized clinical trial demonstrated that the combined use of folic acid and antihypertensive agents, compared with antihypertensive agents alone, significantly reduced the risk of first stroke among patients with hypertension in China [29]. However, in the field of cardiovascular medicine, although a randomized controlled trial has found that vitamin B group supplementation including folate, vitamin B6, and vitamin B12 can reduce plasma Hcy levels, it cannot improve flow-mediated dilatation [30]. Therefore, further multicenter clinical studies are needed to confirm the effectiveness of reducing Hcy and to assess whether reducing Hcy can reduce LVH and cardiovascular events.

It is important to note that circulating Hcy is elevated along with the decline of renal function, which is consistently and inversely correlated with eGFR values. Impaired renal function is a key determinant of high levels of Hcy. Meanwhile, excessive accumulation of Hcy aggravates CKD [21]. Studies suggested that serum creatinine, uric acid, inflammatory mediators’ levels, and oxidative stress increased in renal insufficiency and contributed to the development of LVH [31‒34]. This study also showed that serum uric acid was closely associated with LVMI in hypertensive patients with impaired renal function, indicating that renal insufficiency-related metabolic alterations in addition to blood pressure were involved in higher prevalence of cardiac hypertrophy. Here, our study demonstrated that hypertension and decline of renal function were closely related to high levels of Hcy. To explore the relationship between plasma Hcy and LVH, the data were stratified by the presence of hypertension and condition of renal function, and statistical analysis confirmed that high levels of Hcy were independently correlated with a higher risk of LVH in hypertensive patients with normal renal function, not with impaired renal function. It was speculated that renal insufficiency-related metabolic alterations might have more prominent promoting cardiac hypertrophy effect than Hcy and thus weaken the role of Hcy on LVH. These findings indicated the importance of identifying patients with renal dysfunction when studying the relationship between Hcy and other factors, since the role of Hcy might alter (such as weakened) due to the interference of metabolic alterations in renal insufficiency.

Peer and colleagues found that Hcy was significantly and positively associated with LVM as well as LVMI in females [24]. Ding and colleagues demonstrated that HHcy was an independent risk factor for LVH in patients with hypertension [25]. However, in those studies [6, 23‒25], the study populations were often limited to patients with hypertension, or the percentage of patients with hypertension was high. In addition, patients with end-stage renal failure were excluded, and the majority of study population had normal renal function. As a consequence, researchers could conclude that elevated levels of Hcy were associated with a higher risk of LVH, although the data were not stratified by the condition of renal function in those studies. Therefore, our findings were not conflicting with previous studies.

ACEI and ARB drugs had been shown to prevent and reduce LVH in both human and animal models [35, 36], while their use may decrease eGFR, especially during the first 2 months [37]. However, patients with hypertension in our study usually take antihypertensive agents for a long time with a median duration of hypertension of 7 years, and thus, the use of ACEI and ARB did not decrease eGFR. Moreover, our study showed that the use of ACEI and ARB increased with tertiles of Hcy as well as other antihypertensive drugs such as Ca2+ channel blocker. This was mainly due to the increase in the proportion of hypertensive patients. Statistical analysis adjusting for the use of ACEI and ARB demonstrated that high levels of Hcy were closely associated with a higher prevalence of LVH in hypertensive patients with normal renal function. Therefore, the association between Hcy and LVH was not influenced by their use.

GFR is considered as the best overall index of renal function in health and disease status, but it cannot be measured easily in clinical practice. Instead, GFR is estimated from equations using serum creatinine, age, gender, race, and body size [38, 39]. Currently, the Modification of Diet in Renal Disease (MDRD) Study equation and CKD-EPI equation are both widely used to estimate GFR [12, 40]. However, the CKD-EPI equation is more accurate than MDRD Study equation and could replace it for routine clinical use [12, 41]. In our study, the CKD-EPI equation was used to estimate GFR to better reflect the state of renal function.

It has been proved that elevated resting heart rate is an independent risk factor for cardiovascular disease and associated with higher risks of all-cause and cardiovascular mortality [42, 43]. However, a cohort study of 6,860 subjects (4,203 men, 2,657 women), with mean follow-up period of 3.7 ± 1.4 years, showed that resting heart rate was negatively associated with the development of electrocardiographic LVH in men [44]. This study indicated that heart rate was negatively associated with LVMI in patients with both non-hypertension and hypertension, as our previous research showed [45]. Additionally, our recent research also demonstrated that resting heart rate was independently and negatively associated with LVMI in hypertensive patients with resting heart rate ≤80 beats/min [46]. Therefore, further research is needed to ascertain the relationship between resting heart rate and LVH.

As a note, there were still several limitations in this study. First, the sample size was relatively small, particularly for patients with mildly reduced eGFR. Second, participants were outpatients or inpatients so there might exist a selection bias. Circulating levels of Hcy are affected by diet and may vary from one region to another. All patients in our study were from southern China, Fujian province, and therefore, conclusions drawn from this study could not be extended to general population. Third, no causal relationship could be determined from this study due to the innate drawbacks of cross-sectional studies. In addition, it is important to note that the creatinine measurement in our study is performed using Roche enzymatic method, not with the mass spectrometry calibrated standards. The enzymatic method is not a gold standard; however, it could be sufficient, since it is widely utilized [12, 47‒49].

As we know, LVH is a result in response to complicated factors, mainly attributed to increased blood pressure and many humoral stimulations. However, other factors such as duration of hypertension, BMI, and HHcy also have an impact on LVH. Our research shows that there is a significant correlation between high levels of Hcy and LVH before declining renal function, suggesting that early intervention of HHcy before declining renal function may be beneficial to prevent LVH. However, the effectiveness of treatment against HHcy is not well established yet. Further clinical trials are needed to evaluate the efficacy of reducing HHcy and to clarify the role of HHcy on LVH. Moreover, our result also indicates that the remaining renal function needs to be taken into account when explaining the relationship between Hcy and other factors.

In conclusion, there existed an independent correlation between plasma Hcy levels and LVMI in hypertensive patients with normal eGFR. Our study provided evidence for relationships among plasma Hcy levels, LVH, and renal function in a population from southern China.

We thank Dr. Peng Ye for his constant support and encouragement, who has been working at the Department of Developmental and Stem Cell Biology, City of Hope, Duarte, CA 91010.

The study was in accordance with ethical standards formulated in the Helsinki Declaration, and the protocol was approved by the Ethical Committee of the First Affiliated Hospital of Fujian Medical University (2016-136). All participants provided written informed consent to have their data used in research. Written informed consent was obtained from the parent/legal guardian of participants prior to the study.

The authors declare no conflicts of interest.

This work was supported by the grants from Startup Fund for scientific research, Fujian Medical University (Grant No. 2020QH1052) and National Natural Science Foundation of China (Grant No. 82170355 and 81870316).

Lingyu Zhang contributed to the conception and design of the work; analysis and interpretation of data for the work; and drafting the work. Tingjun Wang contributed to the design of the work and critical revision. Yihua Shen, Li Luo, and Guoyan Xu contributed to critical revision. Liangdi Xie contributed to critical revision of the manuscript and final version approval. All authors read and approved the final manuscript.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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