Impact of Metabolic Syndrome on Flu Vaccine Efficacy

Study Overview
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Brief Summary
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Metabolic syndrome (MetS) is a cluster of metabolic conditions associated with obesity that predispose individuals to coronary heart diseases and diabetes but obesity has been shown to increase the risks of other diseases like cancer and asthma. Studies have also shown that obesity increases the risk of severe influenza infection and associated death and reduces the efficacy of influenza vaccine in the obese population but yet, the molecular mechanisms have not been described. The investigators are thus hypothesizing that differences in the innate immune responses between individual with or without metabolic syndrome impact viral infection and vaccine outcome. The investigators will perform seasonal influenza vaccination in people with or without metabolic syndrome to determine if the late adaptive response assessed by antibodies titers is different between the two groups and correlates with the early immune response assessed by gene expression profile in whole blood cells. The project proposed by the investigators will contribute to a better understanding of the inflammatory phenotype associated with metabolic syndrome and establish for the first time if it affects the immune protection against infectious diseases and particularly against influenza virus infection. The results will be important to determine if the population affected by metabolic syndrome should receive anti-influenza treatment in priority in the context of a severe influenza epidemic.

Detailed Description
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The development of industrialization with increased food consumption and sedentarity has given rise to an obesity pandemic, which affects up to 30% of the population in countries like US, these populations being at greater risk for cardiovascular diseases, and diabetes. More than obesity per se, visceral obesity is associated with metabolic diseases that cluster together and clinically defined metabolic syndrome. MetS comprises individuals with at least three of the 5 of the following factors: abdominal obesity, high blood triglycerides, low HDL (good cholesterol), high blood pressure and elevated fasting glucose. Metabolic syndrome is associated with a low-grade inflammation characterized by an infiltration of immune cells particularly in the adipose tissue, the liver and the pancreas that is thought to be responsible for the induction of insulin resistance. It is thought that obesity predisposes to other diseases such as cancer, asthma but only little attention has been given to infectious diseases. Studies have shown that obesity increases the risk of severe influenza infection and associated death and reduces the efficacy of influenza vaccine in the obese population but yet, the molecular mechanisms have not been described. Immune dysfunctions associated with obesity are suspected to play a major role but obesity is often associated with respiratory disorders that could directly explain the increased susceptibility to influenza infection. Also, metabolically healthy obesity is less associated with inflammation. Therefore, the investigators would like to focus particularly on metabolic syndrome, and determine how it influences immune response to viruses. The investigators are thus hypothesizing that differences in the innate immune responses between individual with or without metabolic syndrome impact viral infection and vaccine outcome. Recent studies involving complex biological analysis and computational modeling have shown that the ability of an individual to positively respond to influenza vaccine can be molecularly predicted by looking at markers in the blood cells. The investigators will perform seasonal influenza vaccination in people with or without metabolic syndrome to determine if the late adaptive response assessed by antibodies titers is different between the two groups and correlates with the early immune response assessed by gene expression profile in whole blood cells. Healthy nutritional habits along with increased physical activities should be best at preventing the development of metabolic syndrome but socio-economical issues are slowing the implementation of these changes. Therefore, as metabolic syndrome is raising public health concerns, it is important to understand why the metabolic syndrome affects susceptibility to diseases.

Official Title
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Impact of Metabolic Syndrome on Flu Vaccine Efficacy

Conditions
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Immune Deficiency, Metabolic Syndrome

Intervention / Treatment
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* Biological: Influenza vaccine


Participation Criteria
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Eligibility Criteria
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Inclusion criteria for the METABOLIC SYNDROME COHORT( Participants must have 3 or more of the following 5 risk factors): Abdominal Obesity, given as a waist circumference: Men >102 cm (>40 in) Women > 88 cm (>35 in) Triglycerides >150 mg/dl HDL Cholesterol: Men < 40 mg/dl Women < 50 mg/dl Blood Pressure >130/ >85 mm Hg - or controlled on antihypertensive medication Fasting Glucose > 100 mg/dl Inclusion criteria for the HEALTHY CONTROLS (Participants must have all of the requirements below) : body mass index 18.5 - 25 kg/m2 HDL female > 50 mg/dL, male > 40 mg/dL fasting glucose < 100 mg/dL triglycerides <150 mg/dL, waist circumference of a female < 88 cm, male < 102 cm) - Blood pressure < or = to 120/80 (based on an average of 3 readings taken 5 minutes apart after the consent form has been signed) Exclusion criteria : Currently undergoing treatment for the metabolic syndrome The average of 2 BP readings > 150/90 (based on 2 B/Ps taken at screening visit 1). Hepatitis A, B and C NSAIDs and/or Aspirin ingestion within the last 14 days Self-reported history of any active autoimmune diseases Self-reported ingestion of statins within the last 3 months Self-reported antibiotic use within the last 3 months Anti-inflammatory drugs including biologics and corticosteroids within last 3 months( nasal spray and topical applications are OK)or Omega 3 Fatty Acids. Self-reported hx of cancer treatment within the last year Allergy to eggs History of Guillain-Barre syndrome Pregnant ( determined by point of care testing at screening visit 1). HIV positive Self-reported history of flu vaccination within the past 3 months. Any self-reported infection in the week of the visit except the first two visits (Screening visit 1 and Screening visit 2) and the last visit (Study visit #5) that could be rescheduled. Any medical, psychological or social condition that, in the opinion of the Investigator, would jeopardize the health or well-being of the participant during any study procedures or the integrity of the data. Study Population Description The population from which the groups will be selected are resident of New York City.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Influenza vaccine in metabolic syndrome<br>Influenza vaccine | Biological: Influenza vaccine<br>* Influenza vaccine administered intramuscularly (IM), 1 time only, on visit 3<br>|
| Experimental: Influenza vaccine in healthy controls<br>Influenza vaccine | Biological: Influenza vaccine<br>* Influenza vaccine administered intramuscularly (IM), 1 time only, on visit 3<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Antibody Response D28 | Measured by hemagglutination inhibition assay | 28 days after vaccination compare to baseline (screening visit 1) pre-vaccination | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Gene Expression Profiling D1 | Analyze by RNA-seq | 1 day post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination) | 
| Gene Expression Profiling D28 | Analyze by RNA-seq | 28 days post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination) | 
| Gene Expression Profiling D90 | Analyze by RNA-seq | 90 days post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination) | 
| Antibody Response D90 | Measured by hemagglutination inhibition assay | 90 days after vaccination compare to day 28 |