<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03237598</url>
  </required_header>
  <id_info>
    <org_study_id>DDS and MetS</org_study_id>
    <nct_id>NCT03237598</nct_id>
  </id_info>
  <brief_title>Gender Difference of Metabolic Syndrome and Its Association Between Dietary Diversity at Different Ages</brief_title>
  <official_title>China Nutrition and Health Survey</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Nanjing Medical University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Nanjing Medical University</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      With the development of economics in China, the dietary diversity got higher. Meanwhile, the&#xD;
      prevalence of metabolic syndrome (MetS) raised up as well. To investigate the gender&#xD;
      difference of getting MetS and its various associations with dietary diversity at different&#xD;
      ages.Data of adults(n=4308) aged 18+ y with three consecutive 24-h recalls and complete&#xD;
      co-variates information were extracted from Chinese Nutrition and Health Survey in 2009.&#xD;
      Modified Dietary Diversity Score (DDS) was adopted to capture the diversity of diet. MetS was&#xD;
      defined by the harmonized criteria. Multivariable adjusted logistic regression was carried&#xD;
      out to detect the association between DDS and MetS and its components for young, middle aged&#xD;
      and elderly adults by a cross-sectional study.&#xD;
&#xD;
      More detailed information can be found in Pubmed,PMID: 24341753 (The China Health and&#xD;
      Nutrition Survey, 1989-2011.).&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Population and data collection Chinese Nutrition and Health Survey （CHNS） was implemented by&#xD;
      the University of North Carolina at Chapel Hill (UNC-CH) and the Chinese Institute of&#xD;
      Nutrition and Food Safety (INFS), and China Center for Disease Control and Prevention (CCDC).&#xD;
      This survey was approved by the Institutional Review Board of aforementioned institutions.&#xD;
      All participants provided the written informed consent. Detailed information about this&#xD;
      survey and laboratory examinations has been described elsewhere .&#xD;
&#xD;
      Dietary assessment and dietary diversity score Individual daily food consumption data were&#xD;
      collected through face-to-face interviews for 3 consecutive days which are randomly&#xD;
      distributed within one week. Trained field interviewers helped each participants recall the&#xD;
      food that they consumed at home and outside in a 24-h period and recorded the codes (listed&#xD;
      in the Food Composition Table of China), types, amounts, and locations of consumption for&#xD;
      each food item by using food models and pictures. More detailed information about the dietary&#xD;
      data in CHNS has been previously reported (The China Health and Nutrition Survey,&#xD;
      1989-2011,PMID: 24341753 .).&#xD;
&#xD;
      In line with previous studies, the investigator combine the original 12 major food categories&#xD;
      in Chinese Food Composition Table into 6 broad groups (grains, vegetables, fruits,&#xD;
      meat/poultry/seafood, dairy, and beans/eggs/nuts) based on similarities in nutrient&#xD;
      composition and dietary function. Detailed information about DDS has been depicted in&#xD;
      previous study(PMID: 27848939). Foods consumed less than a minimum amount (25g) per day was&#xD;
      excluded to avoid measurement error caused by negligible consumption. Since the consumption&#xD;
      of dairy was generally low in Chinese dietary, the cutoff point was adjusted to 10g per day.&#xD;
      The DDS value ranges from 0 to 6 and higher value indicates greater diversity of diet.&#xD;
&#xD;
      Outcome variables and covariates Waist circumference was measured around the body at the top&#xD;
      of the hipbone, using an unstretched tape over the light cloth. Measurement was conducted&#xD;
      without putting any pressure to body surface, and the value was recorded to the nearest&#xD;
      0.1cm.Height and weight was measured by trained health worker using regularly calibrated&#xD;
      equipments under the instructions of manufacturers. BMI was calculated by dividing the weight&#xD;
      (kg) by the square of the height (m2). Blood pressure was measured after thrice rest in a&#xD;
      seated position, each rest lasts for 5 minutes. It was utilized to calculate the average&#xD;
      value of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Blood sample was&#xD;
      collected after 12-14h overnight fasting from all participants, and was stored in vacationer&#xD;
      tubes. All blood samples were analyzed in the central laboratories of China-Japan Friendship&#xD;
      Hospital. Fasting plasma glucose was measured by glucose oxidase-3'-phosphoadenosine&#xD;
      5'-phosphate (GOD-PAP), using the kit produced by Randox, United Kingdom (UK). Serum&#xD;
      high-density lipoprotein cholesterol (HDL-C) concentration was measured by enzymatic method;&#xD;
      serum triglyceride levels was measured by cholesterol oxidase peroxidase -&#xD;
      3'-phosphoadenosine 5'-phosphate (CHOD-PAP); these two testing reagent were produced by the&#xD;
      Kyowa, Japan. The last three indices were measured by the Hitachi 7600 machine. Detailed&#xD;
      information about blood collection and test are presented in appendix 1.&#xD;
&#xD;
      Information about sex, age, income per capita, and educational level (Primary, middle and&#xD;
      high) can be found in previous study, total energy intake, smoking status (0= currently not&#xD;
      smoking, 1= currently smoking) and drinking status (0= did not drink in the past year, 1=&#xD;
      drank alcohol in the past year), and residential area (urban and rural, north and south) were&#xD;
      collected as co-variables. Physical activity was defined according to occupation (1=light&#xD;
      physical activity, working in a sitting or standing position, such as office worker, teacher;&#xD;
      2=moderate physical activity, such as student or driver; 3= heavy physical activity, such as&#xD;
      farmers, loader, miner), and adjusted in the model as well. Adults were categorized into&#xD;
      three age groups according to the World Health Organization (WHO) standards (young: ≥18 &amp;&#xD;
      ≤45; adult: &gt;45 &amp; ≤60; old: &gt;60).&#xD;
&#xD;
      Statistical analysis First, in order to investigate whether the association between dietary&#xD;
      diversity and MetS varies between men and women, an interaction term between sex and DDS was&#xD;
      added into the multivariable linear model. Accordingly, the descriptive statistical were&#xD;
      presented at each tertiles of DDS for each sex(1st, ≤3; 2nd, 4; 3rd, 5-6). Continuous&#xD;
      variates are presented as means ± standard deviations and categorical variables are presented&#xD;
      as percentages.&#xD;
&#xD;
      The association between DDS and MetS and its individual components was detected by&#xD;
      multivariable-adjusted logistic regression, both the OR values and 95% CI were presented.&#xD;
      Sex, income per capita, educational level, physical activity, age and age squared, smoking&#xD;
      and drinking status, fat share, total energy intake, and residential location were adjusted&#xD;
      in the multivariable regression model. Income and total energy intake were measured in&#xD;
      logarithm. The association between predicted probability of MetS and age was mapped using&#xD;
      kernel weighted local polynomial smoothing; both the fitted probability and 95% confidence&#xD;
      interval were presented. The linear trend of odds ratio for DDS was tested by taking DDS as&#xD;
      continuous variable in the multivariable logistic regression.&#xD;
&#xD;
      All analyses were performed with Stata multi processing (MP) 13.0 (Stata Crop, USA) and&#xD;
      P&lt;0.05 was considered as statistical significance.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 1, 2009</start_date>
  <completion_date type="Actual">July 2017</completion_date>
  <primary_completion_date type="Actual">December 31, 2009</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Metabolic syndrome</measure>
    <time_frame>through study completion, an average of 1 year</time_frame>
    <description>MetS was defined according to the harmonized definition of the International Diabetes Federation and other organizations , that three or more out of five following criteria are considered as MetS: (1) central adiposity (WC &gt;102 cm in men and &gt;88cm in women); (2) serum HDL-C &lt; 50 mg/dL in women or &lt; 40 mg/dL in men; (3) serum triglyceride levels &gt; 150 mg/dL; (4) SBP ≥ 130mm Hg or DBP ≥ 85mm Hg; (5) fasting plasma glucose ≥100 mg/dL.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">4308</enrollment>
  <condition>Metabolic Syndrome</condition>
  <condition>Dietary Diversity</condition>
  <condition>Age</condition>
  <arm_group>
    <arm_group_label>male</arm_group_label>
    <description>young (≥18 and ≤45), adult (45~60), and old (&gt;60) , n=1960</description>
  </arm_group>
  <arm_group>
    <arm_group_label>female</arm_group_label>
    <description>young (≥18 and ≤45), adult (45~60), and old (&gt;60) , n=2348</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        Finally, 4308 participants were included in the present study, of which 2348 are female,&#xD;
        and 1960 are male.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  In the present study, participants were extracted from 2009, as the biochemical data&#xD;
             was only available in 2009. Only adults (≥18 years old) with complete information on&#xD;
             food consumption and biochemical data were included in our analysis (n=5118).&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  We excluded women who were pregnant or lactating (n=41). To avoided the distortion of&#xD;
             outlier, adults with implausible daily energy intake (&gt;7000kcal or &lt;520kcal) were&#xD;
             censored (n=4). Meanwhile, adults who have a history of metabolic related disease,&#xD;
             such as myocardial infarction (n=15), diabetes (n=90), and apoplexy (n=31) were&#xD;
             excluded, because their diet might be changed after diagnostic disease. In addition,&#xD;
             we also excluded people who take antihypertensive drugs (n=507). After matching food&#xD;
             data with biochemical and anthropometric data and biochemical data, individuals with&#xD;
             incomplete information was removed (n=122).&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>99 Years</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Hui Wang, Dr</last_name>
    <role>Principal Investigator</role>
    <affiliation>Nanjing Medical University</affiliation>
  </overall_official>
  <verification_date>August 2017</verification_date>
  <study_first_submitted>July 26, 2017</study_first_submitted>
  <study_first_submitted_qc>August 1, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 2, 2017</study_first_posted>
  <last_update_submitted>August 1, 2017</last_update_submitted>
  <last_update_submitted_qc>August 1, 2017</last_update_submitted_qc>
  <last_update_posted type="Actual">August 2, 2017</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Nanjing Medical University</investigator_affiliation>
    <investigator_full_name>Hui Wang</investigator_full_name>
    <investigator_title>Principal Investigator</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Metabolic Syndrome</mesh_term>
    <mesh_term>Syndrome</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>The bio-marker data is confidential, researchers need to apply for the analysis.</ipd_description>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03237598
org study id: DDS and MetS
nct id: NCT03237598
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
With the development of economics in China, the dietary diversity got higher. Meanwhile, the
      prevalence of metabolic syndrome (MetS) raised up as well. To investigate the gender
      difference of getting MetS and its various associations with dietary diversity at different
      ages.Data of adults(n=4308) aged 18+ y with three consecutive 24-h recalls and complete
      co-variates information were extracted from Chinese Nutrition and Health Survey in 2009.
      Modified Dietary Diversity Score (DDS) was adopted to capture the diversity of diet. MetS was
      defined by the harmonized criteria. Multivariable adjusted logistic regression was carried
      out to detect the association between DDS and MetS and its components for young, middle aged
      and elderly adults by a cross-sectional study.

      More detailed information can be found in Pubmed,PMID: 24341753 (The China Health and
      Nutrition Survey, 1989-2011.).
Population and data collection Chinese Nutrition and Health Survey （CHNS） was implemented by
      the University of North Carolina at Chapel Hill (UNC-CH) and the Chinese Institute of
      Nutrition and Food Safety (INFS), and China Center for Disease Control and Prevention (CCDC).
      This survey was approved by the Institutional Review Board of aforementioned institutions.
      All participants provided the written informed consent. Detailed information about this
      survey and laboratory examinations has been described elsewhere .

      Dietary assessment and dietary diversity score Individual daily food consumption data were
      collected through face-to-face interviews for 3 consecutive days which are randomly
      distributed within one week. Trained field interviewers helped each participants recall the
      food that they consumed at home and outside in a 24-h period and recorded the codes (listed
      in the Food Composition Table of China), types, amounts, and locations of consumption for
      each food item by using food models and pictures. More detailed information about the dietary
      data in CHNS has been previously reported (The China Health and Nutrition Survey,
      1989-2011,PMID: 24341753 .).

      In line with previous studies, the investigator combine the original 12 major food categories
      in Chinese Food Composition Table into 6 broad groups (grains, vegetables, fruits,
      meat/poultry/seafood, dairy, and beans/eggs/nuts) based on similarities in nutrient
      composition and dietary function. Detailed information about DDS has been depicted in
      previous study(PMID: 27848939). Foods consumed less than a minimum amount (25g) per day was
      excluded to avoid measurement error caused by negligible consumption. Since the consumption
      of dairy was generally low in Chinese dietary, the cutoff point was adjusted to 10g per day.
      The DDS value ranges from 0 to 6 and higher value indicates greater diversity of diet.

      Outcome variables and covariates Waist circumference was measured around the body at the top
      of the hipbone, using an unstretched tape over the light cloth. Measurement was conducted
      without putting any pressure to body surface, and the value was recorded to the nearest
      0.1cm.Height and weight was measured by trained health worker using regularly calibrated
      equipments under the instructions of manufacturers. BMI was calculated by dividing the weight
      (kg) by the square of the height (m2). Blood pressure was measured after thrice rest in a
      seated position, each rest lasts for 5 minutes. It was utilized to calculate the average
      value of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Blood sample was
      collected after 12-14h overnight fasting from all participants, and was stored in vacationer
      tubes. All blood samples were analyzed in the central laboratories of China-Japan Friendship
      Hospital. Fasting plasma glucose was measured by glucose oxidase-3'-phosphoadenosine
      5'-phosphate (GOD-PAP), using the kit produced by Randox, United Kingdom (UK). Serum
      high-density lipoprotein cholesterol (HDL-C) concentration was measured by enzymatic method;
      serum triglyceride levels was measured by cholesterol oxidase peroxidase -
      3'-phosphoadenosine 5'-phosphate (CHOD-PAP); these two testing reagent were produced by the
      Kyowa, Japan. The last three indices were measured by the Hitachi 7600 machine. Detailed
      information about blood collection and test are presented in appendix 1.

      Information about sex, age, income per capita, and educational level (Primary, middle and
      high) can be found in previous study, total energy intake, smoking status (0= currently not
      smoking, 1= currently smoking) and drinking status (0= did not drink in the past year, 1=
      drank alcohol in the past year), and residential area (urban and rural, north and south) were
      collected as co-variables. Physical activity was defined according to occupation (1=light
      physical activity, working in a sitting or standing position, such as office worker, teacher;
      2=moderate physical activity, such as student or driver; 3= heavy physical activity, such as
      farmers, loader, miner), and adjusted in the model as well. Adults were categorized into
      three age groups according to the World Health Organization (WHO) standards (young: ≥18 &
      ≤45; adult: >45 & ≤60; old: >60).

      Statistical analysis First, in order to investigate whether the association between dietary
      diversity and MetS varies between men and women, an interaction term between sex and DDS was
      added into the multivariable linear model. Accordingly, the descriptive statistical were
      presented at each tertiles of DDS for each sex(1st, ≤3; 2nd, 4; 3rd, 5-6). Continuous
      variates are presented as means ± standard deviations and categorical variables are presented
      as percentages.

      The association between DDS and MetS and its individual components was detected by
      multivariable-adjusted logistic regression, both the OR values and 95% CI were presented.
      Sex, income per capita, educational level, physical activity, age and age squared, smoking
      and drinking status, fat share, total energy intake, and residential location were adjusted
      in the multivariable regression model. Income and total energy intake were measured in
      logarithm. The association between predicted probability of MetS and age was mapped using
      kernel weighted local polynomial smoothing; both the fitted probability and 95% confidence
      interval were presented. The linear trend of odds ratio for DDS was tested by taking DDS as
      continuous variable in the multivariable logistic regression.

      All analyses were performed with Stata multi processing (MP) 13.0 (Stata Crop, USA) and
      P<0.05 was considered as statistical significance.
observational model: Other
time perspective: Cross-Sectional
measure: Metabolic syndrome
time frame: through study completion, an average of 1 year
description: MetS was defined according to the harmonized definition of the International Diabetes Federation and other organizations , that three or more out of five following criteria are considered as MetS: (1) central adiposity (WC >102 cm in men and >88cm in women); (2) serum HDL-C < 50 mg/dL in women or < 40 mg/dL in men; (3) serum triglyceride levels > 150 mg/dL; (4) SBP ≥ 130mm Hg or DBP ≥ 85mm Hg; (5) fasting plasma glucose ≥100 mg/dL.
arm group label: male
description: young (≥18 and ≤45), adult (45~60), and old (>60) , n=1960
arm group label: female
description: young (≥18 and ≤45), adult (45~60), and old (>60) , n=2348
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 99 Years
last name: Hui Wang, Dr
role: Principal Investigator
affiliation: Nanjing Medical University
responsible party type: Principal Investigator
investigator affiliation: Nanjing Medical University
investigator full name: Hui Wang
investigator title: Principal Investigator
mesh term: Metabolic Syndrome
mesh term: Syndrome
sharing ipd: No
ipd description: The bio-marker data is confidential, researchers need to apply for the analysis.