xml: <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/NCT05975541</url>
    </required_header>
    <id_info>
      <org_study_id>CE277/2022</org_study_id>
      <nct_id>NCT05975541</nct_id>
    </id_info>
    <brief_title>Susceptibility to Infectious Diseases in Obesity</brief_title>
    <acronym>SIDERALE</acronym>
    <official_title>Susceptibility to Infectious Diseases in Obesity: an Endocrine, Translational, Sociological Evaluation</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Azienda Ospedaliero Universitaria Maggiore della Carita</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Azienda Ospedaliero Universitaria Maggiore della Carita</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The role of intestinal microbiota is becoming ever more important in the context of obesity,&#xD;
        type II diabetes (T2D), and infectious disorders as represented by the emerging discipline&#xD;
        &quot;therapeutic microbiology&quot;. The gut microbiota is strictly interconnected with obesity and&#xD;
        T2D playing also an important role in immune system regulation.&#xD;
  &#xD;
        Obesity and diabetes can lead to chronic inflammation, which results in the secretion of&#xD;
        pro-inflammatory cytokines like IL-6, IL-1, and TNF-alpha, causing immune system alteration&#xD;
        which predisposes patients with obesity and T2D to chronic infections. Therefore, the&#xD;
        principal aim of the study is to investigate changes in gut microbiota composition between&#xD;
        patients with chronic infections or not, so as to attribute to specific phyla the formation&#xD;
        of the infections in these patients.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Obesity is nowadays a relevant public issue, with more than 650 million people affected&#xD;
        worldwide. By definition, obesity is characterized by excessive calorie intake and lowering&#xD;
        energy expenditure favored today by the increased sedentary lifestyle and junk food&#xD;
        availability. Obesity represents a risk factor for cardiovascular diseases, neoplasm, and&#xD;
        first of all type II diabetes. Nonetheless, obesity increases susceptibility to infectious&#xD;
        disorders such as urinary tract infections, periodontitis, herpes virus infection, and also&#xD;
        Covid-19. During the pandemic, obese patients were interested in the severe form of Covid-19&#xD;
        and also they presented less response to the vaccine.&#xD;
  &#xD;
        Because of insulin resistance and chronic inflammation, typical of obese patients, T2D is a&#xD;
        classical comorbidity presented by these individuals. Together these two pathologies lead to&#xD;
        immune system deregulation and a high predisposition to secondary infections. A third player&#xD;
        is the gut microbiota, recent discoveries demonstrated as intestinal microbiota is strictly&#xD;
        interconnected with obesity and diabetes and with the circadian clock. Being the gut&#xD;
        microbiota an important regulator of the immune system changes in gut microbiota composition&#xD;
        induced by diet, obesity, and T2D can be reflected in the impairment of the immune system.&#xD;
  &#xD;
        The purpose of the present study is the investigation of the gut microbiota signature in&#xD;
        patients presenting obesity and T2D with or without infectious diseases. The idea is the&#xD;
        identification of microbiota composition about pathological, metabolomic, and socio-economic&#xD;
        features. Obtained results from this study could be used for the identification of new&#xD;
        therapeutic targets involved in the development of these pathologies.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Anticipated">October 1, 2023</start_date>
    <completion_date type="Anticipated">October 1, 2025</completion_date>
    <primary_completion_date type="Anticipated">February 25, 2025</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Control</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Omic signature differences between group of case and group of control</measure>
      <time_frame>Changes in microbiota composition between time 0 months (start point of the study) and 12 months (end point of the study)</time_frame>
      <description>it is expected to find differences in the signature of the gut microbiota between patients with chronic infections and those not having infections</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Immune system differences between group of case and group of control</measure>
      <time_frame>Changes in the level and type of pro-inflammatory cytokines between basal time, 0 months , 6 and 12 months (end of the study) in both case and control group</time_frame>
      <description>DIfferences among pro-inflammatory cytokines (IL-6, IL-1, IL-10, IL-17, IL-18, TNF-alpha)</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Patient card for the evaluation of the pharmacological therapy</measure>
      <time_frame>changes between time 0 months (basal time) and 12 months (end of the study)</time_frame>
      <description>Changes in pharmacological therapy for the treatment of diabetes, obesity, or concomitant infections due to aggravation or resolution of the pathology will be investigated through the use of a patient card.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Identification of the oral microbiota</measure>
      <time_frame>changes in oral microbiota between the two different groups at time 0 months, 6 months and 12 months</time_frame>
      <description>collection of oral microbiota by swab both in the control group and in the case</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Number of white blood cells</measure>
      <time_frame>changes in WBC biochemical levels between time 0 months, 6 months and 12 months in both case and control groups</time_frame>
      <description>Use of the leucocyte formula for evaluating the number of white blood cells (WBC) in the patient's blood in both case and group.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Blood cells count</measure>
      <time_frame>changes in blood cells count between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Evaluation of the blood cells with particular attention to the red blood cells in both control and case groups.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Lipid profile identification</measure>
      <time_frame>changes in lipid profile between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Evaluation of the total cholesterol, LDL, and triglycerides through blood sample collection in both case and control groups.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Uric acid concentration</measure>
      <time_frame>changes in uric acid levels between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Evaluation of the levels of uric acid through blood sample collection in patients.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Evaluation of urine parameters</measure>
      <time_frame>changes in microalbuminuria and creatinuria levels between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Detection of the levels of microalbuminuria and creatinuria in patients of both group through urine collection and analysis.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Concentration of vitamin D</measure>
      <time_frame>changes in vitamin D levels between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Detection of Vitamin D levels in patients, through blood sample collection.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Detection of immunoglobulins</measure>
      <time_frame>changes in immunoglobulins profile between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Total immunoglobulins evaluation (IgE, IgA, IgD, IgG, IgM) in patients through blood sample collection. The analysis will be performed on both case and control groups.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>QPE index evaluation</measure>
      <time_frame>Changes in QPE index level between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Evaluation of album, alpha-1 globulin, alpha-2 globulin, beta-globulin, and gamma-globulin through protein electrophoresis (QPE) in both case and control groups.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Concentration of electrolyte levels</measure>
      <time_frame>Changes in the electrolyte levels between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Detection of phosphorus and calcium levels in patients, through blood sample collection of both case and control groups.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Detection of haptoglobins level</measure>
      <time_frame>Changes in haptoglobins levels between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Evaluation of the haptoglobins levels in patients through blood sample collection.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Fecal calprotectin levels identification</measure>
      <time_frame>Changes in fecal calprotectin levels between time 0 months, 6 months and 12 months in both case and control group</time_frame>
      <description>Detection of fecal calprotectin levels in stool samples collected from patients in both groups.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>body weight monitoring</measure>
      <time_frame>changes between time 0 months and time 12 months</time_frame>
      <description>observation of body weight differences in both case and control groups.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>BMI classification</measure>
      <time_frame>change in BMI classification between time 0 months (basal) and 12 months (end of the study)</time_frame>
      <description>individuals will be obese and therefore classify based on BMI, during the study this classification could be change</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>evaluation of waist and hip circumference</measure>
      <time_frame>changes between time 0 months. 6 months and 12 months</time_frame>
      <description>changes in waist and hip circumference in both groups</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>assessment of dietary habits</measure>
      <time_frame>changes in dietary habits between time 0 and time 12 months.</time_frame>
      <description>identification of dietary habits in control groups and in the case group. Detection of the differences in the dietary habits between the two groups through the use of some specific and validated questionnaires (24h recall and Predimed).&#xD;
  A 24-hour dietary recall (24HR) questionnaire to provides information about the consumption of food groups of particular interest, such as the total intake of fruits, vegetables, or added sugars.&#xD;
  Predimed is a questionnaire based on adherence to the Mediterranean Diet. There is a final score that allows to identify those patients that follow this kind of nutritional regimen. Each question (if true for Mediterranean Diet) gives 1 point; the final score is structured as follows:&#xD;
  ≤5 Low adherence 6 - 9= medium adherence &gt;10 good adherence</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>assessment of the circadian rhythm trough MEQ questionnaire</measure>
      <time_frame>changes in circadian rhythm between time 0 months, 6 months and 12 months</time_frame>
      <description>Detection of the circadian rhythm through the Morningness Eveningness Questionnaire (MEQ) in patients of both control and case groups.&#xD;
  MEQ is a self-assessment questionnaire developed to measure whether a person's circadian rhythm (biological clock) produces peak alertness in the morning, in the evening, or in between.&#xD;
  The standard MEQ consists of 19 multiple-choice questions, each having four or five response options.&#xD;
  Responses to the questions are combined to form a composite score that indicates the degree to which the respondent favors morning versus evening. The score is in a range between 16 and 86. If the score is lower or equal to 41 the patient is classified as an &quot;evening&quot; type; equal to or higher than 59, the patient is a &quot;morning&quot; type; between 42 and 58 is a &quot;middle&quot; phenotype.</description>
    </secondary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">80</enrollment>
    <condition>Obesity</condition>
    <condition>Type2diabetes</condition>
    <condition>Gut Microbiota</condition>
    <condition>Chronic Disease</condition>
    <arm_group>
      <arm_group_label>case (patients having chronic infections)</arm_group_label>
      <description>Individuals aged between 18-65 years, presenting obesity (BMI between 30-40 kg/m2), type 2 diabetes, and chronic infections (i.e. urinary tract infections, periodontitis, herpetic infections) at the basal time.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>control (patients without chronic infections)</arm_group_label>
      <description>Individuals aged between 18-65 years, presenting obesity (BMI between 30-40 kg/m2) and type 2 diabetes.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Observational</intervention_name>
      <description>No intervention will be carried out in this study. Patients from both groups will be monitored for 12 months through observations at time 0, time 6 months, and 12 months. Questionnaires, good clinical practice samples, and anthropometric measurements will be made to monitor changes in the two groups.</description>
      <arm_group_label>case (patients having chronic infections)</arm_group_label>
      <arm_group_label>control (patients without chronic infections)</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          The population of interest for this study will be recruited by the endocrinology of the&#xD;
          hospital &quot;Maggiore della Carità&quot; of Novara. The patients recruited will be equally divided&#xD;
          into the two groups according to the presence or not of concurrent infections and will be&#xD;
          adequately matched by age, sex, and characteristics in order to maintain a certain&#xD;
          homogeneity of the study.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  age between 18-65 years&#xD;
  &#xD;
            -  ability to understand the study protocol&#xD;
  &#xD;
            -  obesity with BMI between 30-40 kg/m2&#xD;
  &#xD;
            -  affects by type II diabetes&#xD;
  &#xD;
            -  presence/absence of chronic infections&#xD;
  &#xD;
            -  the possibility of equality in the two groups (matched for sex, age, and therapies)&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  psychological conditions that reduced the ability the comprehension of the study&#xD;
               protocol&#xD;
  &#xD;
            -  subjects undergoing a diet-therapeutic regimen;&#xD;
  &#xD;
            -  subjects with the possibility of developing diabetic foot in the successive months&#xD;
  &#xD;
            -  patients with relapsing infections&#xD;
  &#xD;
            -  pregnancy&#xD;
  &#xD;
            -  bariatric surgery&#xD;
  &#xD;
            -  oncological and hematological pathologies&#xD;
  &#xD;
            -  Hypogonadism&#xD;
  &#xD;
            -  severe immunodepression&#xD;
  &#xD;
            -  vaccination in the last two weeks&#xD;
  &#xD;
            -  Alcohol or drug abuse&#xD;
  &#xD;
            -  antibiotic therapy&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>65 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Flavia Prodam, Prof.MD</last_name>
      <phone>+39 0321 660 693</phone>
      <email>flavia.prodam@med.uniupo.it</email>
    </overall_contact>
    <location>
      <facility>
        <name>Maggiore della Carità Hospital</name>
        <address>
          <city>Novara</city>
          <zip>28100</zip>
          <country>Italy</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Flavia Prodam, Prof.MD</last_name>
        <phone>+39 0321 660 693</phone>
        <email>flavia.prodam@med.uniupo.it</email>
      </contact>
    </location>
    <location_countries>
      <country>Italy</country>
    </location_countries>
    <link>
      <url>https://www.who.int/data/gho/data/indicators/indicator-details/GHO/prevalence-of-obesity-among-adults-bmi-=-30-(age-standardized-estimate)-(-)</url>
      <description>Related Info</description>
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    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 12, 2023</study_first_submitted>
    <study_first_submitted_qc>August 2, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 4, 2023</study_first_posted>
    <last_update_submitted>August 2, 2023</last_update_submitted>
    <last_update_submitted_qc>August 2, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 4, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Azienda Ospedaliero Universitaria Maggiore della Carita</investigator_affiliation>
      <investigator_full_name>Flavia Prodam</investigator_full_name>
      <investigator_title>Associated Prof. in Clinical Nutrition and MD</investigator_title>
    </responsible_party>
    <keyword>obesity</keyword>
    <keyword>adult</keyword>
    <keyword>type 2 diabetes</keyword>
    <keyword>intestinal microbiota</keyword>
    <keyword>chronic infections</keyword>
    <keyword>immune system</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Communicable Diseases</mesh_term>
      <mesh_term>Infections</mesh_term>
      <mesh_term>Obesity</mesh_term>
      <mesh_term>Diabetes Mellitus, Type 2</mesh_term>
      <mesh_term>Chronic Disease</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT05975541
  org study id: CE277/2022
  nct id: NCT05975541
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The role of intestinal microbiota is becoming ever more important in the context of obesity,
        type II diabetes (T2D), and infectious disorders as represented by the emerging discipline
        "therapeutic microbiology". The gut microbiota is strictly interconnected with obesity and
        T2D playing also an important role in immune system regulation.
  
        Obesity and diabetes can lead to chronic inflammation, which results in the secretion of
        pro-inflammatory cytokines like IL-6, IL-1, and TNF-alpha, causing immune system alteration
        which predisposes patients with obesity and T2D to chronic infections. Therefore, the
        principal aim of the study is to investigate changes in gut microbiota composition between
        patients with chronic infections or not, so as to attribute to specific phyla the formation
        of the infections in these patients.
  Obesity is nowadays a relevant public issue, with more than 650 million people affected
        worldwide. By definition, obesity is characterized by excessive calorie intake and lowering
        energy expenditure favored today by the increased sedentary lifestyle and junk food
        availability. Obesity represents a risk factor for cardiovascular diseases, neoplasm, and
        first of all type II diabetes. Nonetheless, obesity increases susceptibility to infectious
        disorders such as urinary tract infections, periodontitis, herpes virus infection, and also
        Covid-19. During the pandemic, obese patients were interested in the severe form of Covid-19
        and also they presented less response to the vaccine.
  
        Because of insulin resistance and chronic inflammation, typical of obese patients, T2D is a
        classical comorbidity presented by these individuals. Together these two pathologies lead to
        immune system deregulation and a high predisposition to secondary infections. A third player
        is the gut microbiota, recent discoveries demonstrated as intestinal microbiota is strictly
        interconnected with obesity and diabetes and with the circadian clock. Being the gut
        microbiota an important regulator of the immune system changes in gut microbiota composition
        induced by diet, obesity, and T2D can be reflected in the impairment of the immune system.
  
        The purpose of the present study is the investigation of the gut microbiota signature in
        patients presenting obesity and T2D with or without infectious diseases. The idea is the
        identification of microbiota composition about pathological, metabolomic, and socio-economic
        features. Obtained results from this study could be used for the identification of new
        therapeutic targets involved in the development of these pathologies.
  observational model: Case-Control
  time perspective: Prospective
  measure: Omic signature differences between group of case and group of control
  time frame: Changes in microbiota composition between time 0 months (start point of the study) and 12 months (end point of the study)
  description: it is expected to find differences in the signature of the gut microbiota between patients with chronic infections and those not having infections
  measure: Immune system differences between group of case and group of control
  time frame: Changes in the level and type of pro-inflammatory cytokines between basal time, 0 months , 6 and 12 months (end of the study) in both case and control group
  description: DIfferences among pro-inflammatory cytokines (IL-6, IL-1, IL-10, IL-17, IL-18, TNF-alpha)
  measure: Patient card for the evaluation of the pharmacological therapy
  time frame: changes between time 0 months (basal time) and 12 months (end of the study)
  description: Changes in pharmacological therapy for the treatment of diabetes, obesity, or concomitant infections due to aggravation or resolution of the pathology will be investigated through the use of a patient card.
  measure: Identification of the oral microbiota
  time frame: changes in oral microbiota between the two different groups at time 0 months, 6 months and 12 months
  description: collection of oral microbiota by swab both in the control group and in the case
  measure: Number of white blood cells
  time frame: changes in WBC biochemical levels between time 0 months, 6 months and 12 months in both case and control groups
  description: Use of the leucocyte formula for evaluating the number of white blood cells (WBC) in the patient's blood in both case and group.
  measure: Blood cells count
  time frame: changes in blood cells count between time 0 months, 6 months and 12 months in both case and control group
  description: Evaluation of the blood cells with particular attention to the red blood cells in both control and case groups.
  measure: Lipid profile identification
  time frame: changes in lipid profile between time 0 months, 6 months and 12 months in both case and control group
  description: Evaluation of the total cholesterol, LDL, and triglycerides through blood sample collection in both case and control groups.
  measure: Uric acid concentration
  time frame: changes in uric acid levels between time 0 months, 6 months and 12 months in both case and control group
  description: Evaluation of the levels of uric acid through blood sample collection in patients.
  measure: Evaluation of urine parameters
  time frame: changes in microalbuminuria and creatinuria levels between time 0 months, 6 months and 12 months in both case and control group
  description: Detection of the levels of microalbuminuria and creatinuria in patients of both group through urine collection and analysis.
  measure: Concentration of vitamin D
  time frame: changes in vitamin D levels between time 0 months, 6 months and 12 months in both case and control group
  description: Detection of Vitamin D levels in patients, through blood sample collection.
  measure: Detection of immunoglobulins
  time frame: changes in immunoglobulins profile between time 0 months, 6 months and 12 months in both case and control group
  description: Total immunoglobulins evaluation (IgE, IgA, IgD, IgG, IgM) in patients through blood sample collection. The analysis will be performed on both case and control groups.
  measure: QPE index evaluation
  time frame: Changes in QPE index level between time 0 months, 6 months and 12 months in both case and control group
  description: Evaluation of album, alpha-1 globulin, alpha-2 globulin, beta-globulin, and gamma-globulin through protein electrophoresis (QPE) in both case and control groups.
  measure: Concentration of electrolyte levels
  time frame: Changes in the electrolyte levels between time 0 months, 6 months and 12 months in both case and control group
  description: Detection of phosphorus and calcium levels in patients, through blood sample collection of both case and control groups.
  measure: Detection of haptoglobins level
  time frame: Changes in haptoglobins levels between time 0 months, 6 months and 12 months in both case and control group
  description: Evaluation of the haptoglobins levels in patients through blood sample collection.
  measure: Fecal calprotectin levels identification
  time frame: Changes in fecal calprotectin levels between time 0 months, 6 months and 12 months in both case and control group
  description: Detection of fecal calprotectin levels in stool samples collected from patients in both groups.
  measure: body weight monitoring
  time frame: changes between time 0 months and time 12 months
  description: observation of body weight differences in both case and control groups.
  measure: BMI classification
  time frame: change in BMI classification between time 0 months (basal) and 12 months (end of the study)
  description: individuals will be obese and therefore classify based on BMI, during the study this classification could be change
  measure: evaluation of waist and hip circumference
  time frame: changes between time 0 months. 6 months and 12 months
  description: changes in waist and hip circumference in both groups
  measure: assessment of dietary habits
  time frame: changes in dietary habits between time 0 and time 12 months.
  description: identification of dietary habits in control groups and in the case group. Detection of the differences in the dietary habits between the two groups through the use of some specific and validated questionnaires (24h recall and Predimed).
  A 24-hour dietary recall (24HR) questionnaire to provides information about the consumption of food groups of particular interest, such as the total intake of fruits, vegetables, or added sugars.
  Predimed is a questionnaire based on adherence to the Mediterranean Diet. There is a final score that allows to identify those patients that follow this kind of nutritional regimen. Each question (if true for Mediterranean Diet) gives 1 point; the final score is structured as follows:
  ≤5 Low adherence 6 - 9= medium adherence >10 good adherence
  measure: assessment of the circadian rhythm trough MEQ questionnaire
  time frame: changes in circadian rhythm between time 0 months, 6 months and 12 months
  description: Detection of the circadian rhythm through the Morningness Eveningness Questionnaire (MEQ) in patients of both control and case groups.
  MEQ is a self-assessment questionnaire developed to measure whether a person's circadian rhythm (biological clock) produces peak alertness in the morning, in the evening, or in between.
  The standard MEQ consists of 19 multiple-choice questions, each having four or five response options.
  Responses to the questions are combined to form a composite score that indicates the degree to which the respondent favors morning versus evening. The score is in a range between 16 and 86. If the score is lower or equal to 41 the patient is classified as an "evening" type; equal to or higher than 59, the patient is a "morning" type; between 42 and 58 is a "middle" phenotype.
  arm group label: case (patients having chronic infections)
  description: Individuals aged between 18-65 years, presenting obesity (BMI between 30-40 kg/m2), type 2 diabetes, and chronic infections (i.e. urinary tract infections, periodontitis, herpetic infections) at the basal time.
  arm group label: control (patients without chronic infections)
  description: Individuals aged between 18-65 years, presenting obesity (BMI between 30-40 kg/m2) and type 2 diabetes.
  intervention type: Other
  intervention name: Observational
  description: No intervention will be carried out in this study. Patients from both groups will be monitored for 12 months through observations at time 0, time 6 months, and 12 months. Questionnaires, good clinical practice samples, and anthropometric measurements will be made to monitor changes in the two groups.
  arm group label: case (patients having chronic infections)
  arm group label: control (patients without chronic infections)
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 65 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: Flavia Prodam, Prof.MD
  phone: +39 0321 660 693
  email: flavia.prodam@med.uniupo.it
  facility: 
  status: Recruiting
  contact: 
  country: Italy
  url: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/prevalence-of-obesity-among-adults-bmi-=-30-(age-standardized-estimate)-(-)
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  responsible party type: Principal Investigator
  investigator affiliation: Azienda Ospedaliero Universitaria Maggiore della Carita
  investigator full name: Flavia Prodam
  investigator title: Associated Prof. in Clinical Nutrition and MD
  mesh term: Communicable Diseases
  mesh term: Infections
  mesh term: Obesity
  mesh term: Diabetes Mellitus, Type 2
  mesh term: Chronic Disease