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Abstract

BACKGROUND:

Research on the genetic mechanisms of hypertension has been a hot topic in the cardiovascular field.

OBJECTIVE:

To study the correlation between senile hypertension and traditional Chinese medicine (TCM) constitution and lipoprotein lipase (LPL) gene polymorphism and to provide the theoretical basis for TCM prevention and treatment of hypertension.

METHODS:

The elderly population in communities in Shanghai (hypertensive: 264 cases; non-hypertensive: 159 cases) was taken as the research object. Essential data and information on TCM constitution were collected. The LPL gene mutation was detected using the second-generation sequencing method. Statistical analysis was performed to clarify the relationship between hypertension and senile hypertension. The correlation of TCM constitution with risk factors and LPL gene polymorphisms was studied.

RESULTS:

The primary TCM constitutions in the hypertension group were phlegm-dampness constitution (51.52%), yin-deficiency constitution (17.42%), balanced constitution (15.53%), and yin-deficiency (9.43%). Logistic regression analysis showed that the phlegm-dampness constitution ( $P<$ 0.05, OR $=$ 2.587) and yin-deficiency constitution ( $P<$ 0.01, OR $=$ 2.693) were the risk constitutions of hypertension in the elderly. A total of 37 LPL gene mutation loci (SNP: 22; new discovery: 15) were detected in the LPL gene, and the mutation rates of rs254, rs255, rs3208305, rs316, rs11570891, rs328, rs11570893, and rs13702 were relatively high, which were 26.24%, 26.24%, 16.08%, 14.66%, 13.24%, 12.06%, and 10.64%. In the phlegm-dampness group, the proportion of rs254 CC type, rs255 TT type, and rs13702 TT type in the hypertensive group (77.21%, 77.21%, and 93.38%) was higher than that in the non-hypertensive group (56.41%, 56.41%, and 82.05%), The difference was statistically significant ( $P<$ 0.05).

CONCLUSION:

The phlegm-dampness constitution and yin-deficiency constitution are the risk factors of hypertension in the elderly; in the phlegm-dampness population, rs254 CC type, rs255 TT type, and rs13702 TT type are the risk factors for elderly hypertension.

Keywords

Elderly hypertension LPL gene TCM constitution

1. Introduction

Hypertension is a common chronic disease that can cause severe damage to the heart, brain, kidney, and other target organs [1, 2]. It has a high incidence and seriously affects the quality of life of patients. It is estimated that 25% of the world’s population suffers from hypertension [3]. It is also associated with increased cardiovascular morbidity and all-cause mortality [4]. If the patient’s blood pressure is not controlled in time, the risk of cardiovascular events significantly increase by 74% [5]. Therefore, effective early intervention of hypertension is an important measure to prevent and treat cardiovascular and other target organ damage.

The concept of traditional Chinese medicine (TCM) constitution originated from the Huangdi Neijing – Inner Canon of the Yellow Emperor, and is used in managing human health. Presently, the TCM constitution proposed by Wang Bing in 762 C.E., is the widely used and accepted classification. Li classified constitutions into nine types based on the performance of the human body, including balanced, yin-deficiency, yang-deficiency, qi-deficiency, blood stasis, phlegm-dampness, damp-heat, qi-stagnation, and inherited unique constitutions [6]. TCM constitution is an objective life phenomenon that shows the comprehensive and relatively stable characteristics of the morphology, physiological function, and psychological state of an individual based on genetic heredity and acquired traits. It reflects the pathophysiological characteristics of different individuals [6]. TCM constitution can reflect the susceptibility and tendency of different individuals to some diseases [7]. Combined with the fact that the constitution is not immutable, the correlation between diseases and TCM constitution can be studied and measures adopted based on individual conditions, reduce the bias of the TCM constitution, delay the development of diseases, and prevent complications [8].

Research on the genetic mechanisms of hypertension has been a hot topic in the cardiovascular field. Many genes are associated with hypertension. With the improvement of genetic testing technology, the research on LPL gene mutations is no longer limited to its effect on lipid metabolism but has gradually extended to its susceptibility to cardiovascular diseases [9]. In this study, we used the second-generation sequencing method to detect polymorphic loci in all target regions of the LPL gene and to screen and analyze the detected mutations. This study offers some theoretical support for the use of TCM in the prevention and treatment of hypertension by analyzing the correlation between senile hypertension, TCM composition, and LPL gene polymorphism.

2. Data and methods

2.1 Study population

Between April 2019 and September 2020, 504 elderly individuals who underwent health check-ups and senile hypertensive patients who visited the outpatient clinics of community health service centers in Shanghai, were enrolled in the study. They provided informed consent before joining the study. Among these individuals, 81 with incomplete information were excluded. The remaining 423 cases were divided into two groups – hypertensive group ( $n=$ 264) and non-hypertensive group ( $n=$ 159). The age of senile hypertensive patients ranged between 65 and 91 years old (average age: 71.70 $\pm$ 5.76 years); the age of senile non-hypertensive individuals ranged between 65 and 91 years old (average age: 70.48 $\pm$ 5.54 years).

2.2 Diagnostic criteria

2.2.1 Diagnostic criteria of Western medicine

For the definition of senile hypertension, we referred to the Chinese Guidelines for Prevention and Treatment of Hypertension (Revised Edition 2018) and Chinese Guidelines for Management of Senile Hypertension (Edition 2019). Definition: age: $\geqslant$ 65 years old; systolic blood pressure (SBP) $\geqslant$ 140 mmHg and/or diastolic blood pressure (DBP) $\geqslant$ 90 mmHg (without antihypertensive drugs, blood pressure was measured three times in the clinic on different days); isolated systolic hypertension: SBP $\geqslant$ 140 mmHg and DBP $<$ 90 mmHg; patients with a history of hypertension who are currently using antihypertensive medications [10]. The blood pressure classification of the participants is displayed in Table 1.

Table 1
Blood pressure classification

Classification	SBP (mmHg)		DBP (mmHg)
Normal blood pressure	$<$ 120	And	$<$ 80
High normal value	120–139	And/or	80–89
Hypertension	$\geqslant$ 140	And/or	$\geqslant$ 90
Grade 1 hypertension (mild)	140–159	And/or	90–99
Grade 2 hypertension (moderate)	160–179	And/or	100–109
Grade 3 hypertension (severe)	$\geqslant$ 180	And/or	$\geqslant$ 110
Isolated systolic hypertension	$\geqslant$ 140	And	$<$ 90

Note: When systolic and diastolic blood pressure are of different grades, the higher grade is used as the standard.

2.2.2 TCM constitution classification criteria

The constitution was determined by referring to the Constitution Classification and Determination Table of Traditional Chinese Medicine in the Constitution Classification and Determination in Traditional Chinese Medicine (ZYYXH/T157-2009) (Table 2) issued by the Chinese Academy of Traditional Chinese Medicine on March 26, 2009 and implemented on April 9, 2009. Two or more attending TCM physicians interviewed the participants and filled out all the investigations in the Constitution Classification and Determination Table of Traditional Chinese Medicine. Each investigation question was scored on five levels. The original score and conversion score were calculated to determine the constitution according to the standard. The original score $=$ the sum of the scores of each item; the conversion score $=$ [(original score-number of items)/(number of items $\times$ 4)] $\times$ 100. The balanced constitution is normal, while the other eight constitutions are biased [11].

Table 2
Constitution classification and determination table of traditional Chinese medicine

Constitution	Condition	Determination result
Balanced constitution	Conversion score $\geqslant$ 60 points	Yes
	Conversion scores of the other 8 constitutions are all $<$ 30.
	Conversion score $\geqslant$ 60 points	Basically
	Conversion scores of the other 8 constitutions are all $<$ 40.
	Those who do not meet the above conditions	No
Biased constitution	Conversion score $\geqslant$ 40 points	Yes
	Conversion score 30–39 points	Inclined
	Conversion score $<$ 30 points	No

2.2.3 Inclusion and exclusion criteria

Inclusion criteria of hypertensive group: $>$ 65 years old; met the diagnostic criteria of hypertension; signed the informed consent form. Exclusion criteria of hypertensive group: with secondary hypertension; with comorbidities of severe liver and kidney diseases, respiratory diseases, and psychiatric disorders; with comorbidities of hematological diseases, tumors, autoimmune diseases, or other hereditary diseases.

Inclusion criteria of non-hypertensive group: non-hypertensive individuals over 65 years old who underwent the health check-up. Exclusion criteria of non-hypertensive group: with severe cardiovascular and cerebrovascular diseases, liver and kidney diseases, diagnosed with respiratory system and psychiatric disorders before being selected; with hematological diseases, tumors, autoimmune diseases or other hereditary diseases; with a recent history of surgery and trauma.

2.3 Study methods

2.3.1 General clinical data

General clinical data were collected by questionnaire. The general information of the participants was recorded, including name, age, height, weight, body mass index (BMI), blood pressure, past history, history of smoking, and history of alcohol consumption. A face-to-face investigation of the participants was conducted by two or more attending TCM physicians. The physicians filled out the Constitution Classification and Determination Table of Traditional Chinese Medicine, determined the TCM constitution, and recorded it.

2.3.2 Collection of blood specimens

The study participants had a light meal the day before their blood was drawn, fasted after 8:00 p.m., and venous blood was collected before 8:00 a.m. the next morning. The collected blood specimen was partially used to detect primary biochemical indexes (including blood sugar, blood lipid, and liver and kidney function). The remaining specific was used for DNA extraction for LPL second-generation sequencing.

2.3.3 Extraction of whole blood DNA

DNA was extracted according to TIANamp Blood DNA Kit (blood genome DNA extraction kit) and stored at $-$ 20 ${{}^{\circ}}$ C after concentration determination.

2.3.4 LPL gene second-generation sequencing

Primer sequence design (Genome and Bioinformation Research Institution of SIBPT (Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai). PCR amplification of target region: Panel A/B 8 $\mu$ L, DNA (20–200) ng x $\mu$ L, 2XM_Enzyme MIX 12.5 $\mu$ L, Nuclease-free water to 25 $\mu$ L (96 ${}^{\circ}$ C 3 min; 96 ${}^{\circ}$ C 30 s, 60 ${}^{\circ}$ C 4 min 18 cycles; 72 ${}^{\circ}$ C 4 min; 10 ${}^{\circ}$ C $+$ $\infty$ ). PCR product purification, introduction of sequencing adapters: 2X M Enzyme Mix 12.5 $\mu$ L, Nuclease-free water 8.5 $\mu$ L, 2 PCR Primer F 2 $\mu$ L, 2 PCR Barcode R 2 $\mu$ L (96 ${}^{\circ}$ C 3 min; 96 ${}^{\circ}$ C 15 s, 58 ${}^{\circ}$ C 15 s, 72 ${}^{\circ}$ C 30 s 18 cycles; 72 ${}^{\circ}$ C 5 min; 10 ${}^{\circ}$ C $+$ $\infty$ ). After two rounds of PCR product recovery and the PCR-amplified fragments were determined as the target fragments, the Illumina MiSeq sequencing platform was used for sequencing and analysis.

2.4 Statistical analysis

Collected data were statistically analyzed using SPSS22.0 software. Firstly, the collected general data were statistically analyzed. The measurement data are expressed by mean $\pm$ standard deviation ( $\bar{x}$ $\pm$ s). The $t$ -test was used for data conforming to normal distribution and homogeneity of variance; ANOVA or non-parametric test was used between multiple samples; the $\chi^{2}$ test was used for counting data. The odds ratio (OR) was analyzed by logistic regression. The $\chi^{2}$ test was used for genetic data to test whether each locus genotype conformed to the Hardy-Weinberg equilibrium law and whether the data represented the population. The test standard was $\alpha=$ 0.05 and $P<$ 0.05 was statistically significant.

3. Results

3.1 Comparison of baseline data between the senile hypertensive group and non-hypertensive group

The primary condition and related indexes of the 264 senile hypertensive patients and 159 non-hypertensive patients were compared. As shown in Table 3, there were no statistically significant differences between the hypertensive group and the non-hypertensive group in gender, smoking, alcohol consumption, exercise, triglyceride (TG), cholesterol (TC), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C) ( $P>$ 0.05).

Table 3
Comparison of baseline data and related indexes between elderly hypertensive and non-hypertensive population

Index	Hypertensive	Non-hypertensive	$t/\chi^{2}$	$P$
Gender (male/female)	117/147	75/84	0.326	0.614
Smoking (yes/no)	37/227	30/129	1.753	0.216
Drinking (yes/no)	35/229	26/133	0.770	0.394
Exercise (yes/no)	181/83	106/53	0.163	0.747
Triglyceride (TG) (mmol/l)	1.77 $\pm$ 1.16	1.61 $\pm$ 0.83	1.504	0.133
Cholesterol (TC) (mmol/L)	4.68 $\pm$ 0.95	4.72 $\pm$ 1.01	$-$ 0.396	0.692
Low-density lipoprotein (LDL-C) (mmol/L)	2.85 $\pm$ 0.61	2.85 $\pm$ 0.65	0.054	0.957
High-density lipoprotein (HDL-C) (mmol/L)	1.43 $\pm$ 0.28	1.51 $\pm$ 0.68	$-$ 1.794	0.074

3.2 Distribution of TCM constitution in the hypertensive group and the non-hypertensive group

In this study, we divided constitutions into nine categories according to TCM constitutions. The primary TCM constitutions of the hypertension group were phlegm-dampness constitution (51.52%), yin-deficiency constitution (17.42%), and balanced constitution (15.53%); the primary constitutions of the non-hypertension group were balanced constitution (49.06%), phlegm-dampness constitution (24.53%), and yin-deficiency constitution (9.43%) (Table 4).

Table 4
Constitution distribution of hypertensive group and non-hypertensive group number (%)

Constitution	Case ( $n$ )	Hypertension (proportion in total number)		Non-hypertension (proportion in total number)
Balanced	119	41	(15.53%)	78	(49.06%)
Qi-deficiency	12	7	(2.65%)	5	(3.14%)
Yang-deficiency	28	18	(6.82%)	10	(6.29%)
Yin-deficiency	61	46	(17.42%)	15	(9.43%)
Phlegm-dampness	175	136	(51.52%)	39	(24.53%)
Dampness-heat	6	5	(1.89%)	1	(0.63%)
Blood stasis	15	7	(2.65%)	8	(5.03%)
Qi stagnation	4	2	(0.76%)	2	(1.26%)
Inherited special	3	2	(0.76%)	1	(0.63%)
Total	423	264		159

3.3 Logistic regression analysis of the nine constitutions in senile hypertension

Among all the included participants, the independent variables were nine TCM constitutions; the response variables were hypertensive or not; the categorical variables were analyzed individually for the corresponding nine constitutions (e.g., non-phlegm-dampness constitution, etc.) (Table 5). Univariate unconditional logistic regression analysis showed that the phlegm-dampness constitution ( $P<$ 0.01, OR $=$ 3.269) and yin-deficiency constitution ( $P<$ 0.05, OR $=$ 2.026) were the risk factors of senile hypertension; the balanced constitution was the protective factor of senile hypertension ( $P<$ 0.01, OR $=$ 0.191). After correcting for gender, age, physical exercise, smoking, and alcohol consumption, phlegm-damp constitution ( $P<$ 0.01, OR $=$ 2.693) and yin-deficiency constitution ( $P<$ 0.05, OR $=$ 2.587) remained the risk factors of senile hypertension, while the balanced constitution was a protective factor of senile hypertension ( $P<$ 0.01, OR $=$ 0.217) (Table 6).

Table 5
Univariate unconditional logistic regression analysis of nine constitutions of senile hypertension

Constitution	Regression coefficient B	Standard error S.E.	Chi-square value Wald	Degree of freedom Df	$P$	OR	95% confidence interval
							Lower	Upper
Balanced	$-$ 1.656	0.232	50.737	1	0.000 ${}^{\ast\ast}$	0.191	0.121	0.301
Qi-deficiency	$-$ 0.176	0.594	0.087	1	0.768	0.839	0.262	2.689
Yang-deficiency	0.086	0.408	0.045	1	0.832	1.090	0.490	2.425
Yin-deficiency	0.706	0.316	4.986	1	0.026 ${}^{\ast}$	2.026	1.090	3.764
Phlegm-dampness	1.185	0.222	28.555	1	0.000 ${}^{\ast\ast}$	3.269	2.117	5.048
Dampness-heat	1.115	1.100	1.028	1	0.311	3.050	0.353	26.346
Blood stasis	$-$ 0.665	0.528	1.590	1	0.207	0.514	0.183	1.446
Qi stagnation	$-$ 0.512	1.005	0.260	1	0.610	0.599	0.084	4.297
Inherited special	0.187	1.229	0.023	1	0.879	1.206	0.108	13.409

Note: ${}^{*}P<$ 0.05, ${}^{**}P<$ 0.01 is statistically significant.

Table 6

Logistic regression analysis of nine constitutions of senile hypertension after correcting factors

Constitution	Regression coefficient B	Standard error S.E.	Chi-square value Wald	Degree of freedom Df	$P$	OR	95% confidence interval
							Lower	Upper
Balanced	$-$ 1.529	0.249	37.852	1	0.000 ${}^{\ast\ast}$	0.217	0.133	0.353
Qi-deficiency	$-$ 0.327	0.615	0.282	1	0.595	0.721	0.216	2.407
Yang-deficiency	0.419	0.429	0.955	1	0.328	1.521	0.656	3.524
Yin-deficiency	$-$ 0.950	0.330	8.269	1	0.004 ${}^{\ast}$	2.587	1.353	4.944
Phlegm-dampness	0.991	0.258	14.719	1	0.000 ${}^{\ast\ast}$	2.693	1.624	4.467
Dampness-heat	1.333	1.114	1.430	1	0.232	3.791	0.427	33.672
Blood stasis	$-$ 0.481	0.541	0.792	1	0.373	0.618	0.214	1.783
Qi stagnation	$-$ 0.481	1.037	0.215	1	0.643	0.618	0.081	4.722
Inherited special	0.067	1.298	0.003	1	0.959	1.069	0.084	13.624

Note: ${}^{*}P<$ 0.05, ${}^{**}P<$ 0.01 is statistically significant.

3.4 Detection results of genotypes of LPL gene mutation loci

In this study, we detected eight loci with high mutation frequency, including 19811897 (rs254), 19811901 (rs255), 19823648 (rs3208305), 19818436 (rs316), 19822810 (rs11570891), 19819724 (rs328), 19823770 (rs11570893), and 19824492 (rs13702) with mutation frequencies of 26.24%, 26.24%, 16.08%, 14.66%, 13.24%, 12.06%, and 10.64%, respectively. All 8 mutant loci were consistent with the Hardy-Weinberg equilibrium test ( $P>$ 0.05).

3.5 Distribution of genotypes in the hypertensive group and non-hypertensive group in the phlegm-dampness population

In the phlegm-dampness population, the rs254CC, rs255TT, and rs13702TT types accounted for a higher percentage in the hypertensive group (77.21%, 77.21%, and 93.38%) than in the non-hypertensive group (56.41%, 56.41%, and 82.05%). The difference was statistically significant ( $P<$ 0.05) (Table 7). There was no statistically significant difference in the distribution of common mutation genotypes in the yin-deficiency population ( $P>$ 0.05) (Table 8).

Table 7
Distribution of genotypes in hypertensive and non-hypertensive groups with the phlegm-dampness constitution

Locus	Genotype	Hypertensive ( $n=$ 136)	Non-hypertensive ( $n=$ 39)	$\chi^{2}$	$P$
rs254 ${}^{*}$	CC	105	22	6.585	0.014
	CG $+$ GG	22	17
rs255 ${}^{*}$	TT	105	22	6.585	0.014
	TC $+$ CC	22	17
rs320830	AA	116	31	0.760	0.457
	AT $+$ TT	20	8
rs316	CC	118	32	0.550	0.445
	CA $+$ AA	18	7
rs11570891	CC	119	31	1.589	0.205
	CT $+$ TT	17	8
rs328	CC	120	33	0.107	0.586
	CG $+$ GG	16	6
rs11670893	GG	118	36	0.435	0.417
	GA $+$ AA	18	3
rs13702 ${}^{*}$	TT	127	32	3.420	0.038
	TC $+$ CC	9	7

Note: ${}^{*}P<$ 0.05, ${}^{**}P<$ 0.01 is statistically significant.

Table 8

Distribution of genotypes in hypertensive and non-hypertensive groups with yin-deficiency constitution

Locus	Genotype	Hypertensive ( $n=$ 46)	Non-hypertensive ( $n=$ 15)	$\chi^{2}$	$P$
rs254	CC	31	11	0.190	0.757
	CG $+$ GG	15	4
rs255	TT	31	11	0.190	0.757
	TC $+$ CC	15	4
rs320830	AA	40	12	0.058	0.676
	AT $+$ TT	6	3
rs316	CC	40	14	0.043	0.670
	CA $+$ AA	6	1
rs11570891	CC	38	11	0.169	0.467
	CT $+$ TT	8	4
rs328	CC	37	12	0.000	1.000
	CG $+$ GG	9	3
rs11670893	GG	40	14	0.043	0.670
	GA $+$ AA	6	1
rs13702	TT	42	13	0.001	0.630
	TC $+$ CC	4	2

4. Discussion

Under most circumstances, senile hypertension is classified in TCM into “dizziness” and “wind-dizziness” based on symptoms. Senile hypertension has a significant prevalence and is closely related to cardiovascular and cerebrovascular diseases. The risk of developing senile hypertension increases with age. With the population aging, community-level prevention and treatment of senile hypertension are crucial.

The TCM constitution was first described in the Inner Canon of the Yellow Emperor (referred to as Neijing). Lesser yang (or Shaoyang), greater yang (or Taiyang), balanced yin and yang, lesser yin (or Shaoyin), and greater yin (or Taiyin) were the five classifications used in Neijing to classify people’s constitutions. These classifications were based on how much yin and yang the human body possessed, its morphology, function, psychology, and other factors. Later, the five classifications were integrated with the five elements theory to form 25 classifications – the “twenty-five classes of yin-yang person.” Thus, the first systematic TCM constitution categorization was created, categorizing constitutions into 25 categories. Due to a lack of systematic research and clinical popularization, the classification of the five-element constitution gradually faded with the research and development of the TCM constitution. Modern scholars have gradually explored different classifications of constitutions. For instance, based on predecessors’ experience and clinical experience, Du et al. created the six classifications and classified the population into six types: strong, weak, cold-leaning, weak, wet-leaning, and stagnant [12]. Currently, the widely accepted and used constitution classification refers to the manifestation classification method proposed by Professor Wang Qi et al., which classifies constitutions into nine types according to the performance of the human body: balanced constitution, qi-deficiency constitution, yang-deficiency constitution, yin-deficiency constitution, phlegm-dampness constitution, dampness-heat constitution, blood stasis constitution, qi stagnation constitution, and inherited unique constitution, which is convenient for clinical application.

In this study, we referred to the constitution classification and determination method formulated by the China Association of Traditional Chinese Medicine and Pharmacy. The primary constitutions of senile hypertension, based on the research and analysis in this study, are phlegm-dampness (51.52%), yin-deficiency 46 cases (17.42%), and balanced constitution (15.53%). Liu et al. included 2,532 cases of elderly people aged 65 and above in a constitution analysis. They found that the TCM constitutions of senile hypertension were mainly the yin-deficiency constitution, phlegm-dampness constitution, and dampness-heat constitution [13]. Hypertension is more common in biased TCM constitutions, with phlegm-dampness constitutions accounting for the most significant proportion, according to the research by Li et al. [14]. Through logistic regression analysis of the nine constitutions, we found that the phlegm-dampness constitution and yin-deficiency constitution were the risk factors of senile hypertension, and the balanced constitution was the protective factor of senile hypertension.

Cited from Danxi’s Mastery of Medicine: “You won’t develop dizziness if you don’t have any phlegm; in the body, phlegm goes together with the fire; dampness phlegm and fire phlegm are both types of phlegm. Phlegm dampness and phlegm fire are essential in developing dizziness. Phlegm is a significant causative element of dizziness. Dizziness is closely related to the spleen, stomach, liver, and kidney. The spleen and stomach belong to the middle energizer. The spleen is the site where phlegm first develops. The spleen provides the material basis for the acquired constitution and is responsible for transporting and transforming the water and grain essence. Spleen transportation and transformation disorder result in failure of stomach qi to descend, retention of dampness, phlegmatic retention in the middle energizer; hypoactivity of spleen yang, and clear yang failing to ascend. Then dizziness will develop. Phlegm goes together with fire and Phlegm, qi-deficiency, and fire jointly cause head dizziness.” Danxi’s Mastery of Medicine holds that besides phlegm-dampness, phlegm-fire also contributes to dizziness.

The onset of dizziness is mainly due to yin deficiency, which leads to deficiency fire. Deficiency fire tends to burn the body fluid and produce phlegm, which leads to dizziness. The results of this study show that phlegm-dampness is dangerous and is the primary constitution in the middle-aged and elderly hypertensive population, which is consistent with the theory that phlegm-dampness causes dizziness.

Cited from Jingyue’s Complete Works: “80% or 90% of dizziness is caused by deficiency, and only 10% or 20% of dizziness is caused by a combination of deficiency, fire, and phlegm.” According to Jingyue’s Complete Works, dizziness is mainly caused by deficiency.

Elderly patients with hypertension suffer from dizziness due to old age, fatigue, deficiency of qi and blood, imbalance of viscera, chronic injury of the liver and kidney, and deficiency of essence and blood. The essence of dizziness is related to yin deficiency of the liver and kidney, and it is the syndrome of deficiency in nature and excess in superficiality. Based on the research on the pathogenesis characteristics of modern literature on TCM for hypertension, Jian et al. defined yin deficiency as the fundamental cause [15]. The results of this study revealed that yin deficiency constitution was the risk factor of senile hypertension, which is consistent with the pathogenesis theory of dizziness. Phlegm-dampness and yin-deficiency constitutions may be susceptible to and are risk factors for senile hypertension. In this study, we provide a theoretical basis for preventing and treating hypertension from the perspective of preventive treatment of diseases in TCM.

Hypertension, one of the senile chronic diseases, impairs target organs (heart, brain, kidney, and others). The pathogenesis of hypertension has long been a hot research topic. Genetic heredity studies on the pathogenesis of hypertension, have helped in detecting the pathogenesis of hypertension and have had a positive effect on preventing and treating hypertension, and provide a theoretical basis for the precise prevention and treatment of hypertension.

Studies on genetic polymorphisms in hypertension are divided into single-gene and multi-gene studies. There are more than 150 genes that have been confirmed to be associated with hypertension regulatory pathways [16]. Multiple genes are associated with blood pressure regulation. These genes have been detected in multiple blood pressure-related systems, including the renin-angiotensin-aldosterone system, the natriuretic peptide system, and the endothelin system. The renin-angiotensin-aldosterone system (RAAS) mainly regulates blood pressure and electrolyte homeostasis [17]. Aldosterone synthase is a crucial enzyme in the RAAS system, and its coding gene polymorphism is closely related to primary hypertension. The CYP2C9*3 allele of the CYP2C9 gene, an essential member of the P450 family, was shown to be related to hypertension and it was suggested that it may elevate the concentration of ARB analogs in the body by affecting the activity of metabolic enzymes [18]. Lipoprotein lipase (LPL) produces free fatty acids through catalytic reactions in lipid metabolism. Some are absorbed, processed into neutral lipids, and stored in adipose tissue, skeletal muscle, and myocardium after oxidation reaction [19]. The human lipoprotein lipase gene consists of 10 exons and 9 introns.

In this study, 283 mutant loci were detected, with exons particularly evident [20]. Current studies on LPL mainly focus on HindIII, S447X, and PvuII locus variants, with the HindIII locus being the more common of the LPL gene variant loci and being closely associated with dyslipidemia. Studies have shown that HindIII polymorphism in the intron 8 region of the LPL gene is significantly associated with lipid metabolism disorders [21]. Surendran et al. found that LPL gene mutation accounted for the highest proportion (34%), in 86 patients with severe hypertriglyceridemia, and Asp36Asn, Asn318Ser, and Ser447X were common gene mutations ${}^{[22]}$ . Studies on the action mechanism of the LPL gene have shown that mutations of C418Y and E412K genes limit the binding ability of LPL to GPIHBP1, thus making it challenging for it to adhere to the inner wall of blood vessels and blocking its function of triglyceride degradation [23].

With the improvement of genetic testing technology, the research on LPL gene mutations is no longer limited to its effect on lipid levels. However, it has gradually shifted to its susceptibility to various cardiovascular diseases [24]. Whether abnormal lipid metabolism (an independent risk factor of hypertension) and LPL (a key enzyme of lipid metabolism) are related to primary hypertension has sparked an interest. Chen et al. [25, 26] included a young and middle-aged population aged 18 to 60 with SNP loci rs253 and rs328 of LPL. They used a case-control study method to find that rs253 locus polymorphism of the LPL gene may be associated with the occurrence of primary hypertension and found that those with genotype CT and TT were more likely to develop primary hypertension than those with genotype CC.

A total of 37 LPL gene mutation loci were detected in this study (SNP: 22; new discovery: 15), with rs254, rs255, rs3208305, rs316, rs11570891, rs328, rs11570893, and rs13702 having higher mutation rates of 26.24%, 26.24%, 16.08%, 14.66%, 13.24%, 12.06%, and 10.64%, respectively. In the phlegm-dampness population, the rs254CC, rs255TT, and rs13702TT types accounted for a higher percentage in the hypertensive group (77.21%, 77.21%, 93.38%) than in the non-hypertensive group (56.41%, 56.41%, 82.05%). The difference was statistically significant ( $P<$ 0.05). There was no statistically significant difference in the distribution of common mutation genotypes in the yin-deficiency population ( $P>$ 0.05). Among the elderly phlegm-damp population, those carrying LPL genes rs254CC, rs255TT, and rs13702TT were more likely to develop hypertension.

The diagnosis and monitoring of hypertension is important for better management of blood pressure and to reduce the risk of developing future cardiovascular diseases [27]. Compared to the data science methods, Artificial intelligence (AI) approaches have revolutionized the way data can be processed and analyzed. At present, the machine learning algorithm has been used for hypertension prediction. Such as, Hung et al. used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN). Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC $=$ 0.851 and 0.837; sensitivity $=$ 1.000 and 1.000; specificity $=$ 0.609 and 0.580; NPV $=$ 1.000 and 1.000; accuracy $=$ 0.766 and 0.721, respectively) [27]. The previous study demonstrated that machine learning combined with traditional hypertension risk factors and genomics can build an efficient hypertension risk prediction model. Pei et al. constructed an essential hypertension risk-predicting model considering both environmental and genetic factors with support vector machine (SVM) [28]. The results showed that after combining environmental and genetic factor, the accuracy improved to 80.1%. And SVM model considering both environmental and genetic factors had better performance than the model with environmental or genetic factors only. From this perspective, although AI method has great advantages in predicting hypertension, its prediction accuracy may be closely related to prediction factors. Therefore, combined with the research results of this paper, the inclusion of TCM information such as hypertension constitution into the prediction factors may help to improve the prediction accuracy.

5. Conclusion

Compared with previous studies, we used the more up-to date second-generation sequencing method to detect polymorphic loci of the LPL gene and mutations, thus we provided some new information to support the use of TCM in the prevention and treatment of hypertension by analyzing the correlation between senile hypertension, TCM composition, and LPL gene polymorphism.

This study however is not without limitations, as the age of included cases was limited to older people aged 65. The sample size was not large, as only the population in Shanghai was collected. The interaction between multiple genes and environmental factors causes hypertension. Genetic polymorphism studies have not been fully elucidated, and the proportion of single genes in the pathogenesis needs further investigation. The TCM constitution is formed by a combination of genetic and acquired traits and is influenced by many factors, such as the environment. Therefore, it is necessary to explore further the correlation between TCM constitution and genetic polymorphism in senile hypertension.

Competing interests

The authors declare that they have no competing interests.

Funding

The study was supported by the Shanghai Pudong New Area Health Commission Science Foundation for Youth (No. PW2021B-19), State Administration of Traditional Chinese Medicine a special research project on the establishment of the national TCM clinical research base (No. JDZX2012114), Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine Foundation for Fostering Talents (No. QMX2021-06), and Seventh Peopleâ€™s Hospital of Shanghai University of Traditional Chinese Medicine Foundation for Fostering Talents (No. GJ2021-08).

Footnotes

Acknowledgments

The authors are grateful to all everyone who helped them with the article.

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Correlation analysis of hypertension,traditional Chinese medicine constitution,and LPL gene polymorphism in the elderly in communities in Shanghai

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Data and methods

2.1 Study population

2.2 Diagnostic criteria

2.2.1 Diagnostic criteria of Western medicine

Table 1 Blood pressure classification

Table 2 Constitution classification and determination table of traditional Chinese medicine

2.3 Study methods

2.3.1 General clinical data

2.3.2 Collection of blood specimens

2.3.3 Extraction of whole blood DNA

2.3.4 LPL gene second-generation sequencing

2.4 Statistical analysis

3. Results

3.1 Comparison of baseline data between the senile hypertensive group and non-hypertensive group

Table 3 Comparison of baseline data and related indexes between elderly hypertensive and non-hypertensive population

Table 4 Constitution distribution of hypertensive group and non-hypertensive group number (%)

Table 5 Univariate unconditional logistic regression analysis of nine constitutions of senile hypertension

3.5 Distribution of genotypes in the hypertensive group and non-hypertensive group in the phlegm-dampness population

Table 7 Distribution of genotypes in hypertensive and non-hypertensive groups with the phlegm-dampness constitution

5. Conclusion

Competing interests

Funding

Footnotes

Acknowledgments

References

Table 1
Blood pressure classification

Table 2
Constitution classification and determination table of traditional Chinese medicine

Table 3
Comparison of baseline data and related indexes between elderly hypertensive and non-hypertensive population

Table 4
Constitution distribution of hypertensive group and non-hypertensive group number (%)

Table 5
Univariate unconditional logistic regression analysis of nine constitutions of senile hypertension

Table 7
Distribution of genotypes in hypertensive and non-hypertensive groups with the phlegm-dampness constitution