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UM: Role of Umami in the Regulation of Healthy Food Choices (2014)

Citation:
 
Study Design:
Class:
- Click here for explanation of classification scheme.
Quality Rating:
Research Purpose:
  • To investigate a possible association between MSG intake and obesity
  • To determine whether a greater MSG intake is associated with a clinically significant weight gain over five years.
Inclusion Criteria:
  • All family members aged three years and older from these households were invited to have fasting blood samples taken
  • Written consents.
Exclusion Criteria:
  • Extreme values of weight change (i.e., 20kg, N=11)
  • Diabetes, stroke or cancer at baseline.
Description of Study Protocol:

Recruitment

2,849 participants.

Design

  • Cross-section study design
  • A rural sample was selected from three randomly selected small towns within each of the six counties of Jiangsu (i.e., Jiangyin, Taichang, Shuining, Jurong, Sihong and Haimen). An urban sample was selected from three randomly chosen streets in the capital cities of the two prefectures, Nanjing and Xuzhou.
  • The six counties and the two prefectures represented a geographically and economically diverse population. In each town and street, two villages or neighborhoods were randomly selected, and ninety households were further selected randomly from each village/neighborhood.
  • All the members in the households were invited to take part in the study. In addition, one-third of the households were interviewed by a trained health worker about their dietary intake, and all family members aged three years and older from these households were invited to have fasting blood samples taken.
  • In 2002, height, weight and dietary information were obtained from 2,849 adults aged 20 years and above, and fasting plasma glucose was measured
  • Of the 2,849 participants, 1,682 were identified for follow-up and 1,492 (88·7 %) actually participated in the follow-up interview. A total of 190 participants refused to participate. Height and weight were obtained from 1,282 (76·2 %) participants (210 finished the interview at home, but missed the measurement in the clinic).
  • The final sample in the study for weight change consisted of 509 men and 718 women (total, N=1,227). Compared with the retained participants, those lost to the follow-up were generally younger (45·5 vs. 49·3 years), but there were no differences in the mean BMI or energy intake.
  • Dietary intake was calculated by adding the glutamate concentrations of all foods and seasonings consumed by an individual per day. Nutrient and vegetable oil intakes were also assessed using a three-day weighed food diary that recorded all the foods consumed by each individual on three consecutive days; this was done to confirm the intake reported from the FFQ data.
  • Dietary patterns were identified by factor analysis based on food intake measured by the FFQ using standard principal component analysis
  • Other lifestyle factors included:
    • Smoking
    • Alcohol consumption
    • Daily commuting
    • Daily time spent on sedentary activities
    • Education
    • Occupation.
  • In both 2002 and 2007, anthropometric measurements such as height, weight, BMI, waist circumference were obtained by the use of standard protocols and techniques.

Statistical Analysis

  • MSG intake was recorded into quartiles
  • Fi2 test was used to compare differences between categorical variables and ANOVA was used to compare differences in continuous variables between the groups
  • Multi-level mixed-effects linear regression was used to determine the association between MSG intake and weight change adjusted for age, education, occupation, active commuting, leisure time physical activity, smoking, alcohol drinking, eating out and energy intake
  • Multi-level logistic regression was used to assess the association between MSG intake and the development of a clinically significant weight gain
  • Household cluster was adjusted in these multi-variate models using the xtmelogit command
  • Tested for linear trend across categories of MSG intake by assigning each participant the median value for the category, and by modeling this value as a continuous variable
  • Food patterns were also included in the multi-variate models to control for the residual confounding
  • All the analyses were performed using STATA 10 (Stata Corporation, College Station, TX, US). Statistical significance was considered when P<0·05 (two-sided).


 

 

Data Collection Summary:

Timing of Measurements

The year 2002 and followed up in 2007.

Dependent Variables

  • MSG intake
  • Nutrient intake
  • Dietary pattern.

Independent Variables

  • Age
  • Sex
  • Multiple lifestyle factors
  • Anthropometric measurements
  • Energy intake.
Description of Actual Data Sample:
  • Initial N: 2,849
  • Attrition (final N): 1,682 were identified for follow-up; 1,492 (88.7%) completed the study
  • Age: 45.5 vs. 49.3 years
  • Ethnicity: China.

Anthropometrics

  • Height
  • Weight
  • BMI
  • Waist circumference.

Location
China.

Summary of Results:
  • The mean intake of MSG for the entire population was 3.8±4.3g per day. Of the 1,227 participants, 72 reported no use of MSG and median intakes across the quartiles were 0.8g, 2.0g, 3.7g and 6.9g per day, respectively
  • MSG intake was positively associated with fat intake (P<0.001), but was inversely associated with carbohydrate intake (P<0.001)
  • No significant difference in energy and protein intake was found across MSG intake quartiles
  • Total glutamate was the same in the first, second and third quartiles, but was on average 37% greater in the fourth quartile (P<0.001)
  • Rice intake was greater among individuals in the higher quartiles of MSG intake (P<0.001). Intakes of fruit and vegetables were not different across levels of MSG intake
  • The prevalence of smoking and alcohol drinking was higher among individuals in the higher quartiles of MSG intake (P<0.001)
  • Cross-sectionally, there was an inverse association between MSG intake and weight status and BMI; body weight and BMI tended to decrease across quartiles of MSG intake (all P<0.05). At baseline, the overall prevalence of overweight or obesity (BMI 25kg/m2 or greater) was 29%. Only 9% of the sample were obese (BMI 28kg/m2 or greater)
  • Across quartiles of MSG intake, there was a significant decrease in the prevalence of obesity (P<0.016). However, this association existed after adjusting for socio-demographic factors and dietary patterns in logistic regression. OR for obesity across quartiles of MSG were 1.0, 0.91 (0.54, 1.55), 0.87 (0.48, 1.58) and 0.56 (0.29, 1.09; P for trend, 0.005).
  • When we used all the available data including those lost to follow-up, the baseline association between MSG and obesity was NS: OR across quartiles of MSG intake were 1, 0.93 (0.66, 1.32), 1.01 (0.72, 1.43) and 0.71 (0.49, 1.04; P for trend=0.142)
  • The mean five-year weight gain among all the participants was 0.8±4.7kg
  • The prevalence of 5% weight gain, weight maintenance and weight loss were 26.2%, 59.0% and 14.8%, respectively
  • No association between MSG intake and status of weight change over five years was found
  • The mean MSG intake was 3.8±4.0g among those having 5% weight gain
  • There was a significant difference in the weight and BMI change across quartiles of MSG intake. Compared with those in the first quartile of MSG intake, those in the fourth quartile of MSG intake had lower weight and BMI gain, but had a greater increase in waist circumference. After adjusting for age and sex, there was an inverse association between MSG intake and 5% weight gain. The OR for 5% weight gain across quartiles of MSG intake were 1.0, 0.62 (95% CI, 0.38, 1.01), 0.48 (95% CI, 0.29, 0.80) and 0.58 (95% CI, 0.35, 0.97; P for trend=0.066). After adjusting for lifestyle, demographic factors, energy intake, sex and total glutamate intake, a linear inverse trend between MSG intake and 5% weight gain was observed (Model 4, P=0·028). However, this association disappeared when Model 3 was adjusted for either rice intake (P<0.90) or food patterns (P=0.85).
  • In the multi-variate analysis, traditional food pattern was inversely associated with 5% weight gain (OR, 0.50; 95% CI, 0.39, 0.64; P<0.001).
Author Conclusion:
  • These findings from a Chinese sample from Jiangsu province indicate that an MSG intake that ranges from 2.5 to nine times greater than the MSG intake of "non-users" is not associated with a higher prevalence of obesity or with a clinically significant weight gain over five years, even after adjustment for a number of covariates including dietary patterns
  • We suggest that further research in human subjects is warranted to determine if dietary glutamate intake is associated with weight gain in individuals who have a poor protein and energy status.
Funding Source:
Government: Jiangsu Provincial Natural Science Foundation (BK2008464, and the Jiangsu Provincial Health Bureau, China, and by a research fellowship from the National Health and Medical Research Council of Australia, Centre of Clinical Research Excellence in Nutritional Physiology, Interventions and Outcomes
Reviewer Comments:
  • As it is a cross-sectional study, there are limitations. People who had poor protein had weight gain associated with glutamate dietary intake. This association needs to be studied in detail. No association was observed with MSG and weight.
  • There are other dietary and lifestyle factors that may influence weight gain. Investigators were not blinded for the outcomes. Hence, double blind placebo controlled studies are encouraged.
Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) Yes
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) Yes
 
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? N/A
  2.3. Were health, demographics, and other characteristics of subjects described? N/A
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? N/A
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? N/A
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) N/A
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? Yes
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? N/A
  4.1. Were follow-up methods described and the same for all groups? N/A
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) N/A
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? N/A
  4.4. Were reasons for withdrawals similar across groups? N/A
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? N/A
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) No
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? No
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? N/A
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? Yes
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? Yes
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? Yes
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? Yes
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? Yes
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes