You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background {#Sec1}
==========

As with other chronic diseases, hypertension is prevalent in China \[[@CR1]\]. Data from the China National Nutrition and Health Survey in 2002 showed that the prevalence of hypertension was 25% in Northeast, 27% in North, 17% in Northwest, 12% in Southwest, 19% in East and 17% in Middle South \[[@CR2]\]. This study also confirmed that the prevalence of hypertension was higher in urban compared with rural areas in both gender \[[@CR2]\]. We performed a multistage cluster random sampling of a group of 29,970 adult residents from 2005 to 2006 and found that the overall prevalence of hypertension was increasing to 36.2% \[[@CR3]\]. Previous study investigated that the prevalence of hypertension among middle-aged and old adults in rural Northwest in China from 1982 to 2010 increased from 18.4% to 30.5% \[[@CR4]\].

Framingham Heart Study confirmed that hypertensive individuals had more additional risk factors than their counterparts \[[@CR5]\]. As we know, MetS is a cluster of cardiovascular risk factors including hypertriglyceridemia, elevated blood pressure, abdominal obesity, increasing fasting glucose and low HDL-C which are all closely linked to insulin resistance \[[@CR6]\]. Furthermore, there was study confirmed that the frequency of cardiometabolic risks associating with MetS was greater when hypertension was presented \[[@CR7]\].

With the development of economy, high prevalence of MetS becomes an important public health problem in China. The China Health and Nutrition Survey carried in 2009 reported that the prevalence of MetS reached 21.3%, 18.2% and 10.5% based on definitions of revised NCEP ATPIII, IDF and CDS criteria, respectively \[[@CR8]\]. Our previous study conducted during 2004--2006 in rural Northeast China found that the prevalence of MetS was higher in the hypertensive patients than in the general population and the overall prevalence of MetS was 36.2%, higher than the general level \[[@CR9]\].

In the past ten years, the rural economy in China has achieved a rapid development. However, there was no study focused on the temporal trend of the prevalence and updating the possible risk factors of MetS among hypertensive populations. We hypothesized that huge changes in the prevalence, risk factors of MetS among hypertensive populations might happen in rural Northeast China during this 10 years. Therefore, the objective of this study was to update the prevalence of MetS as well as their risk factors in hypertensive rural Chinese during 2012--2013.

Methods {#Sec2}
=======

Study population {#Sec3}
----------------

Liaoning Province is located in Northeast China. From January 2012 to August 2013, a representative sample of participants aged ≥ 35 years was selected to characterize the prevalence, incidence and natural history of cardiovascular risk factors in rural areas of Liaoning Province. The study adopted a multi-stage, stratified, random-cluster sampling scheme. In the first stage, three counties (Dawa, Zhangwu and Liaoyang County) were selected from the eastern, southern and northern regions of Liaoning province. In the second stage, one town was randomly selected from each county (for a total of three towns). In the third stage, 8--10 rural villages from each town were randomly selected (for a total of 26 rural villages). Participants who were pregnant or had malignant tumors or mental disorders were excluded from the study. All the eligible permanent residents aged ≥ 35 years from each village were invited to attend the study (a total of 14,016 participants). Of those, 11,956 participants agreed and completed the study to give a response rate of 85.3%. The study was approved by the Ethics Committee of China Medical University (Shenyang, China). All procedures were performed in accordance with ethical standards. Written consent was obtained from all participants after they had been informed of the objectives, benefits, medical items and confidentiality agreement regarding their personal information. For participants who were illiterate, we obtained written informed consent from their proxies. In this report, we used only the data from participants who completed the study, which provided a final sample size of 5866 (2859 men and 3007 women).

Data collection and measurements {#Sec4}
--------------------------------

Data were collected during a single visit to the clinic by cardiologists and trained nurses using a standard questionnaire in a face-to-face interview. Before the survey was performed, we invited all eligible investigators to attend an organized training session. The training included the purpose of this study, how to administer the questionnaire, the standard method of measurement, the importance of standardization and the study procedures. A strict test was administered after this training, and only those who scored perfectly on the test were accepted as investigators in this study. During data collection, our inspectors had further instructions and support.

Data regarding the demographic characteristics, lifestyle risk factors, dietary habits, family income and family history of chronic diseases were obtained during the interview using the standardized questionnaire. The study was guided by a central steering committee with a subcommittee for quality control. Educational level was assessed as completion of primary school or less, middle school or high school and higher. Self-reported sleep duration (including nocturnal and nap duration) was obtained from the questionnaire. The responses were categorized into four groups: ≤7, 7--8, 8--9 and \>9 h/d. Family income was classified as ≤5000, 5000--20,000 and \>20,000 CNY/year. Self-reported sleep duration (including nocturnal and nap duration) was obtained from the questionnaire. The responses were categorized into four groups: ≤7, 7--8, 8--9, and \>9 h/d.

According to American Heart Association protocol, blood pressure (BP) was measured three times at 2-min intervals after at least 5 min of rest using a standardized automatic electronic sphygmomanometer (HEM-907; Omron), which had been validated according to the British Hypertension Society protocol \[[@CR10]\]. The participants were advised to avoid caffeinated beverages and exercise for at least 30 min before the measurement. During the measurement, the participants were seated with their arms supported at the level of the heart. The mean of three BP measurements was calculated and used in all analyses.

Weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, with the participants wearing light-weight clothing and without shoes. Waist circumference (WC) was measured at the umbilicus using a non-elastic tape (to the nearest 0.1 cm), with the participants standing at the end of normal expiration. Body mass index (BMI) was calculated as the weight in kilograms divided by the square root of the height in meters.

Fasting blood samples were collected in the morning after at least 12 h of fasting. Blood samples were obtained from an antecubital vein into Vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA). Fasting plasma glucose (FPG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs) and other routine blood biochemical indexes were analyzed enzymatically using an autoanalyzer. All laboratory equipment was calibrated, and blinded duplicate samples were used for these analyses.

Definitions {#Sec5}
-----------

According to the International Diabetes Federation (IDF) criteria, individuals have the MetS if he/she has central adiposity plus two or more of the following four factors:(1) raised concentration of triglycerides ≥1.7mmol/L or taking abnormal lipid medication; (2)reduced concentration of HDL-C \< 1.03mmol/L in male and HDL-cholesterol \< 1.29mmol/L in female; (3)elevated blood pressure ≥ 130/85mmHg or taking hypertension medication;(4) increasing fasting glucose: ≥ 5.6mmol/L or taking diabetes medication \[[@CR11]\].

Physical activity included occupational and leisure-time physical activity. A detailed description of the methods for assessing physical activity has been presented elsewhere \[[@CR12]\]. Occupational and leisure-time physical activity were merged and regrouped into the following three categories: 1) low---subjects who reported light levels of both occupational and leisure-time physical activity, 2) moderate---subjects who reported moderate or high levels of either occupational or leisure-time physical activity and 3) high---subjects who reported a moderate or high level of both occupational and leisure-time physical activity.

Dietary patterns were assessed by having participants recall the foods they had eaten during the previous year. The questionnaire included questions regarding the average consumption of several food items per week. The reported consumption was quantified approximately in terms of grams per week. Vegetable consumption was assessed on the following scale: rarely = 3, \<1000 g = 2, 1000--2000 g = 1, ≥2000 g = 0, and meat consumption, including red meat, fish and poultry was assessed on the following scale: rarely = 0, \<250 g = 1, 250--500 g = 2 and ≥500 g = 3). A special diet score (vegetable consumption score plus meat consumption score) was calculated for each participant (range 0--6). Higher values of the diet score indicated higher meat consumption, lower vegetable consumption and greater adherence to a Westernized diet, while lower values indicate adherence to the Chinese diet. Similar methods for calculating a diet score can be found in the ATTICA study \[[@CR13]\].

Statistical analysis {#Sec6}
--------------------

Descriptive statistics were calculated for all the variables, including continuous variables (reported as mean values and standard deviations) and categorical variables (reported as numbers and percentages). Differences between healthy and MetS groups were evaluated using Student's t-test, ANOVA, non-parametric test or the χ2-test as appropriate. Multivariate logistic regression analyses were used to identify independent factors of MetS among hypertensive adults with odds ratios (ORs) and corresponding 95% confidence intervals (CIs) calculated. All the statistical analyses were performed using SPSS version 17.0 software, and P values less than 0.05 were considered to be statistically significant.

Results {#Sec7}
=======

Basic characteristics of study population {#Sec8}
-----------------------------------------

The characteristics of the hypertensive rural adults enrolled in this study, as stratified with MetS or not, are shown in Table [1](#Tab1){ref-type="table"}. Compared with the non-MetS group, individuals with MetS had significantly higher levels of systolic BP, diastolic BP, waist circumference, BMI, fasting plasma glucose, triglycerides, total cholesterol, LDL-C and the proportion of treated individuals, graduating from high school education was also higher in the MetS group. However, prevalence of current drinking and smoking, diet score, and levels of HDL-C and the proportion of moderate physical activity were lower in the individuals with MetS. There was no significant differences of mean age, the proportion of Han, sleep duration and annual income between these two groups.Table 1**Characteristics of study population with or without MetS in the rural hypertensive population of Liaoning Province, ChinaVariablesTotalNon-MetSMetS*P-*** **value(n = 5866)(n = 3336)(n = 2530)Age (year)**57.29 ± 10.2457.48 ± 10.5057.04 ± 9.890.109**Ethnicity**0.128 Han5555(94.7)3149(94.4)2406(95.1) Others^a^311(5.3)187(5.6)124(4.9)**Educational status**0.001 Primary school or below3276(55.8)1803(54.0)1473(58.2) Middle school2075(35.4)1248(37.4)827(32.7) High school or above515(8.8)285(8.5)230(9.1)**Physical activity**\<0.001 Light1993(34.0)1025(30.7)968(48.6) Moderate3531(60.2)2134(64.0)1397(55.2) Severe342(5.8)177(5.3)165(6.5)**Annual income (CNY/year)**0.081 ≤5000897(15.3)520(15.6)377(42.0) 5000-200003282(55.9)1895(56.8)1387(54.8) \>200001687(28.8)921(27.6)766(30.3)**Smoking status (%)**\<0.001 Never3492(59.5)1786(53.5)1706(48.9) Former318(5.4)195(5.8)123(4.9) Current2056(35.0)1355(65.9)701(34.1)**Drinking status (%)**\<0.001 Never4237(72.2)2197(65.9)2040(80.6) Former196(3.3)125(3.7)71(2.8) Current1433(24.4)1014(30.4)419(16.6)**Diet score**2.30 ± 1.132.37 ± 1.122.20 ± 1.14\<0.001**Sleep duration (h/d)**7.23 ± 1.767.26 ± 1.757.20 ± 1.780.276**SBP (mmHg)**158.91 ± 19.66157.61 ± 18.87160.62 ± 20.53\<0.001**DBP (mmHg)**88.85 ± 11.1788.14 ± 10.8989.79 ± 11.47\<0.001**BMI (kg/m2)**25.56 ± 3.6623.90 ± 3.0527.76 ± 3.21\<0.001**WC (cm)**84.89 ± 9.7479.82 ± 8.0091.57 ± 7.54\<0.001**TC (mmol/L)**5.42 ± 1.115.30 ± 1.045.57 ± 1.19\<0.001**TG (mmol/L)**1.83 ± 1.661.44 ± 1.182.35 ± 2.02\<0.001**LDL-C (mmol/L)**3.08 ± 0.862.97 ± 0.823.22 ± 0.89\<0.001**HDL-C (mmol/L)**1.42 ± 0.401.53 ± 0.431.28 ± 0.31\<0.001**FPG (mmol/L)**6.16 ± 1.895.83 ± 1.576.60 ± 2.17\<0.001**Antihypertensive medication** Yes1733(29.5)746(22.3)987(39.0)\<0.001Data are expressed as the mean ± SD or as n (%). Abbreviations: BMI, body mass index; WC, waist circumference; CNY, China Yuan (1CNY = 0.161 USD); SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose.^a^ Including some ethnic minorities in China, such as Mongol and Manchu.

Prevalence of MetS and its components {#Sec9}
-------------------------------------

As shown in Figure [1](#Fig1){ref-type="fig"}, approximately 43.1% of the hypertensive adults had MetS (Male: 29.2% ,Female: 56.4%). The highest prevalence of MetS was in the age of 35--44 years in male and in the age of 65--74 years in female. Increased trend of the prevalence of MetS in rural hypertensive Northeastern Chinese for our sample using the same criteria \[[@CR9]\] during 2004--2013 years was observed in Figure [1](#Fig1){ref-type="fig"}.Figure 1**The prevalence of MetS in different age group stratified by gender.** The triangle blot means 2012--2013 years while the circular blot means 2004--2006 years.

Figure [2](#Fig2){ref-type="fig"} presented the prevalence of each component of the MetS. In this sample, the prevalence of abdominal obesity, hypertriglyceridemia, low HDL-C, and increasing fasting plasma glucose was 51.9%, 39.6%, 29.3% and 55.9%. Furthermore, the most common metabolic disorder in our study was increasing fasting plasma glucose and followed by abdominal obesity which was closed to our previous study \[[@CR9]\].Figure 2**The increasing trend of the prevalence of different metabolic components from 2004 to 2013 stratified by age.**

Factors associated with MetS and metabolic components {#Sec10}
-----------------------------------------------------

Table [2](#Tab2){ref-type="table"} shown the ORs for the influencing factors associated with MetS and metabolic components among the hypertensive adults. Individuals who were women \[OR(95%CI):4.78(4.0,5.69)\], 45 years or older \[OR(95%CI):1.27(1.02,1.58) for 45--54 years; OR(95%CI):1.66(1.33,2.08) for 55-64years; OR(95%CI):1.89(1.45,2.47) for 65--74 years, OR(95%CI):1.99(1.36,2.95) for \>75years\], graduated from high school or above \[OR(95%CI):1.30(1.01,1.66)\], obesity \[OR(95%CI):1.60(1.56,1.65)\] and current smoking \[OR(95%CI):1.36(1.16,1.59)\] were more likely to have MetS. And moderate physical activity \[OR(95%CI):0.85(0.73,0.98)\], annual income between 5000--20000 CNY/year \[OR(95%CI):0.79(0.66,0.97)\], higher diet score \[OR(95%CI):0.92(0.86,0.97)\] were found to be inversely associated with the probability of MetS.Table 2**Multiple logistic regression analysis of MetS and the associated factors based on IDF criteria among rural hypertensive population in Liaoning Province, ChinaVariablesOR95%CI*P*** **-valueFemale**4.784.0,5.69\<0.001**Age group** 35-441.00(reference) 45-541.271.02,1.580.036 55-641.661.33,2.08\<0.001 65-741.891.45,2.47\<0.001 \>751.991.36,2.95\<0.001**Ethnicity** Han1.00(reference) Others^a^0.660.48,0.890.006**Educational status** Primary school or below1.00(reference) Middle school1.030.88,1.200.745 High school or above1.301.01,1.660.040**Physical activity** Light1.000(reference) Moderate0.850.73,0.980.028 Severe1.170.87,1.560.301**Annual income (CNY/year)**≤ 50001.000(reference) 5000-200000.790.66,0.970.026 \>200000.890.72,1.120.340**Sleep duration (h/d)** ≤71.00(reference) 7-80.970.83,1.140.743 8-90.910.75,1.110.361 \>91.040.81,1.330.773**Diet score (per 1 unit)**0.920.86,0.970.005**BMI (per 1 unit)**1.601.56,1.65\<0.001**Smoking status (%)** Never1.00(reference) Former1.230.91,1.670.186 Current1.361.16,1.59\<0.001**Drinking status (%)** Never1.00(reference) Former1.060.72,1.560.765 Current0.990.82,1.210.983Abbreviations: OR, odds ratio; 95% CI, 95% confidence interval; CNY, China Yuan (1CNY = 0.161 USD).^a^ Including some ethnic minorities in China, such as Mongol and Manchu.

Discussion {#Sec11}
==========

The present study investigated that 43.1% of the hypertensive adults in rural Northeast China had MetS. Female had significantly higher incidence of MetS than male (56.4% for female and 29.2% for male, *P* \< 0.001). An increasing prevalence of MetS was seen in both gender, especially in male. Increasing fasting glucose and abdominal obesity was the most common metabolic disorder in both gender. Age, gender, education, obesity, annual income, physical activity, current smoking and higher diet score was significantly associated with MetS.

Similar to those previous studies, our study investigated that MetS in hypertensive individuals was more prevalent than in the general population (43.1% vs. 30.9%) using the IDF criteria \[[@CR7]\]. The Korean National Health and Nutrition Examination Survey reported that the prevalence of MetS was doubled in hypertensive population in compared to normotensive population as well \[[@CR14]\]. Other studies also demonstrated that MetS was prevalent in hypertensive adults, like Nigeria (42.5%), India (63.5%), Brazil (82.4%) and Iran (51.6%) \[[@CR14]-[@CR17]\]. Insulin resistance plays a key role in this situation \[[@CR18]\]. The sex-specific prevalence in our study was 29.2% and 56.4% among males and females respectively. Similarly, hypertensive women in Iran (65.3% vs. 33.5%) and India (47.6% vs. 15.1%) had higher possibility to have MetS than men as well \[[@CR16],[@CR19]\]. On one hand, the possible reason for the high prevalence of MetS in hypertensive female might be the menopause \[[@CR20]\]. As seen in our study the highest incidence of MetS in hypertensive female was older than 55 years old. Previous study demonstrated that the incidence of MetS increased with menopause \[[@CR21]\]. On the other hand, traditional Chinese cultural influences discouraged women from outdoor activities and very few women took physical activities. In our study, female had significantly higher incidence of low physical activity than male (40.5% vs. 27.1%, *P* \< 0.001).

Hiram Beltrán-Sánchez and colleagues reported that the prevalence of MetS in the U.S adult population decreased from 25.5% to 22.9% from 1999 to 2010. Moreover, hypertriglyceridemia (33.5% to 24.5%) and elevated blood pressure (32.3% to 24.0%) prevalence also decreased during the past ten years \[[@CR22]\]. At the same time, study carried in Korea confirmed that the prevalence of hypertension as well as MetS among the general population decreased slightly during the past ten years. However, MetS prevalence among the hypertensive population continued to rise \[[@CR23]\]. Our study also reached the same conclusion that MetS in hypertensive adults increased from 2004 to 2012 which was more obvious among older patients in men than in women. One interesting finding was that during the past years, prevalence of MetS in hypertensive increased more drastically in male (from 16.1% to 29.2%) than in female (from 49.6% to 56.4%) using the same criterion in the rural Northeast China. And we still need further study to explain this finding. The high prevalence of MetS among hypertensive individuals required the need for metabolic screening in all newly diagnosed hypertensive individuals, especially in hypertensive males in rural areas.

The most common phenotype of MetS with the component of hypertension was the coexistence of increasing fasting glucose and abdominal obesity in rural Northeast Chinese. This was quite different from other countries like Nigeria, India and America. In Nigerian and Indian, visceral obesity and low HDL-C were the other common cardiovascular risk factors among hypertensive subjects \[[@CR14],[@CR15]\]. While in America, besides elevated blood pressure, abdominal obesity and hypertriglyceridemia was the most common metabolic disorder. The possible reason accounted for this situation might be the high prevalence of diabetes and prediabetes in China. A national study from 2007 to 2008 discovered that 92.4 million Chinese adults with diabetes (50.2 million men and 42.2 million women) and 148.2 million Chinese adults with prediabetes (76.1 million men and 72.1 million women) \[[@CR24]\]. Having such a large population of diabetes and prediabetes given above some experts draw a conclusion that China may bear a higher increasing fasting glucose burden than any other country \[[@CR25]\].

Our study investigated that minority, mostly Mongolian and Huis, had less possibility to have MetS than Han. This conclusion was inline with our previous study that Mongolian had relatively lower triglyceride level than Han \[[@CR26]\]. Due to the small amount of minority in the present research, further study was needed to confirmed this relation. While most studies found an inverse association between education status and MetS, on the contrary, higher prevalence of MetS was positively related with higher education in our study after adjusted for age which somehow the same as Jamaican adults \[[@CR27]\]. We also demonstrated that individuals with moderate physical activity had less possibility to had MetS and the possible reason might be moderate physical activity help improve individual metabolic parameters and lose ones weight \[[@CR28]\]. However, vigorous physical activity was not related to MetS in our study because only 5.8% of the participants took part in vigorous physical activity in rural Northeast China. Annual income was associated with MetS in our study. But the relationship between income and MetS or other cardiovascular risks were still controversial in developing and developed countries \[[@CR29]\]. In compared with never smoking, current smoking increased the risk of MetS which had been well proven by previous study \[[@CR30]\].

Limitations {#Sec12}
-----------

First, our study was a cross-sectional study suggesting that it cannot provide sufficient evidence of causality. Longitudinal studies were required for the further investigation. Second, miss classification might exist because the prevalence of MetS was based on a single assessment of blood. Third, although the researchers had been trained according to a standardized protocol of measurements, there might be incorrect values for the anthropometric indexes due to the single visit. Other limitations include recall bias and the small sample sizes available in some subgroup analyses that reduced the statistical power to detect significant associations. Fourth, in our study, we enrolled only the hypertensive patients which was not comprehensive.

Conclusion {#Sec13}
==========

The prevalence of MetS in hypertensive rural Northeast China had increase dramatically during the past ten years. Over 50% of hypertensive females had MetS in rural Northeast China. Furthermore, the obviously increasing trend of MetS during the past ten years in hypertensive males deserved more attention. Metabolic screening was in-need in all newly diagnosed hypertensive individuals, especially in hypertensive male.

**Competing interests**

The authors declare that they have no competing interests

**Authors' contributions**

SSY analyzed the data and wrote the paper. SSY, XFG, HMY enrolled participants and collected data. LQZ and YXS were responsible for designing the study and performing data analyses. All the authors contributed to the interpretation of data and critical revision and approval of the article.

This study was supported by grants from the "Twelfth Five-Year" project funds (National Science and Technology Support Program of China, Grant \# 2012BAJ18B02) that Pro Yingxian Sun responsible for enable the project completion.