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Impact of Metabolic Syndrome on Flu Vaccine Efficacy

Impact of Metabolic Syndrome on Flu Vaccine Efficacy

Status
Terminated
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT02653495
Enrollment
17
Registered
2016-01-12
Start date
2016-01-31
Completion date
2017-03-31
Last updated
2017-09-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Immune Deficiency, Metabolic Syndrome

Brief summary

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

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.

Interventions

BIOLOGICALInfluenza vaccine

Influenza vaccine administered intramuscularly (IM), 1 time only, on visit 3

Sponsors

Rockefeller University
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes

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.

Design outcomes

Primary

MeasureTime frameDescription
Antibody Response D2828 days after vaccination compare to baseline (screening visit 1) pre-vaccinationMeasured by hemagglutination inhibition assay

Secondary

MeasureTime frameDescription
Gene Expression Profiling D11 day post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination)Analyze by RNA-seq
Gene Expression Profiling D2828 days post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination)Analyze by RNA-seq
Gene Expression Profiling D9090 days post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination)Analyze by RNA-seq
Antibody Response D9090 days after vaccination compare to day 28Measured by hemagglutination inhibition assay

Countries

United States

Participant flow

Recruitment details

Advertising and Research Volunteer Repository Database

Pre-assignment details

Pre-screening took place along with a two-step screening visit for eligibility criteria. Those not meeting eligibility would screen out: 86 prescreened, 28 initial contact for screening.

Participants by arm

ArmCount
Influenza Vaccine in Metabolic Syndrome
Influenza vaccine Influenza vaccine: Influenza vaccine administered intramuscularly (IM), 1 time only, on visit 3
3
Influenza Vaccine in Healthy Controls
Influenza vaccine Influenza vaccine: Influenza vaccine administered intramuscularly (IM), 1 time only, on visit 3
2
Total5

Withdrawals & dropouts

PeriodReasonFG000FG001
Overall StudyLost to Follow-up20
Overall StudyScreening Failure100

Baseline characteristics

CharacteristicInfluenza Vaccine in Metabolic SyndromeInfluenza Vaccine in Healthy ControlsTotal
Age, Categorical
<=18 years
0 Participants0 Participants0 Participants
Age, Categorical
>=65 years
0 Participants0 Participants0 Participants
Age, Categorical
Between 18 and 65 years
3 Participants2 Participants5 Participants
Race and Ethnicity Not Collected0 Participants
Region of Enrollment
United States
3 participants2 participants5 participants
Sex: Female, Male
Female
1 Participants1 Participants2 Participants
Sex: Female, Male
Male
2 Participants1 Participants3 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
deaths
Total, all-cause mortality
0 / 30 / 2
other
Total, other adverse events
1 / 31 / 2
serious
Total, serious adverse events
0 / 30 / 2

Outcome results

Primary

Antibody Response D28

Measured by hemagglutination inhibition assay

Time frame: 28 days after vaccination compare to baseline (screening visit 1) pre-vaccination

Population: Data were not collected

Secondary

Antibody Response D90

Measured by hemagglutination inhibition assay

Time frame: 90 days after vaccination compare to day 28

Population: Data were not collected.

Secondary

Gene Expression Profiling D1

Analyze by RNA-seq

Time frame: 1 day post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination)

Population: Data were not collected.

Secondary

Gene Expression Profiling D28

Analyze by RNA-seq

Time frame: 28 days post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination)

Population: Data were not collected.

Secondary

Gene Expression Profiling D90

Analyze by RNA-seq

Time frame: 90 days post-vaccination compare to baseline (gene expression screening visit #1 and study visit #1 D0 of vaccination)

Population: Data were not collected.

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026