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Table of Contents
ORIGINAL ARTICLE
Year : 2023  |  Volume : 28  |  Issue : 1  |  Page : 25-30

Anthropometric effect of a personalized food avoidance dietary approach to stop hypertension


1 Department of Pharmacology and Therapeutics, College of Medicine, University of Nigeria, Enugu Campus, Nigeria; Chiolive International Medical Research Organization, Enugu, Nigeria
2 Chiolive International Medical Research Organization, Enugu, Nigeria

Date of Submission02-Jul-2022
Date of Decision13-Sep-2022
Date of Acceptance28-Oct-2022
Date of Web Publication13-Dec-2022

Correspondence Address:
Micheal T Okafor
Department of Pharmacology and Therapeutics, College of Medicine, University of Nigeria, Enugu Campus
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmh.IJMH_56_22

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  Abstract 

Background: Body anthropometries are indicators of health and disease. It is universally accepted that it is a useful tool for assessing health status. Objective: The aim of this article is to determine the effect of a personalized food avoidance dietary approach to stop hypertension (PFADASH) on anthropometric parameters: body mass index (BMI), triceps skin fold thickness (TSFT), and abdominal circumference (AC) on study participants. Materials and Methods: This was a longitudinal study and part of an open controlled clinical trial of a PFADASH approved by the University of Nigeria Teaching Hospital Ethics Committee. Anthropometric parameters were compared between study participants with good and poor dietary compliance to a PFADASH. Results: There was no significant difference in the anthropometric parameters: BMI, TSFT, and AC among participants with good and poor dietary compliance to a PFADASH (P > 0.05). Conclusion: There was no negative effect of a PFADASH on anthropometric parameters, despite not being a calorie-restrictive dietary intervention.

Keywords: Anthropometry, clinical trial, dietary compliance, lifestyle modification


How to cite this article:
Chijioke CP, Okafor MT, Okoli UA, Nubia IN, Nwokolo B, Onah IC, Ide C, Effiong-Essieng C, Obiefuna G, Anusiem C. Anthropometric effect of a personalized food avoidance dietary approach to stop hypertension. Int J Med Health Dev 2023;28:25-30

How to cite this URL:
Chijioke CP, Okafor MT, Okoli UA, Nubia IN, Nwokolo B, Onah IC, Ide C, Effiong-Essieng C, Obiefuna G, Anusiem C. Anthropometric effect of a personalized food avoidance dietary approach to stop hypertension. Int J Med Health Dev [serial online] 2023 [cited 2023 Jan 31];28:25-30. Available from: https://www.ijmhdev.com/text.asp?2023/28/1/25/363256




  Introduction Top


Anthropometric measurements are non-invasive quantitative measurements of the body. It is a valuable index for clinical assessment, diagnosis, and monitoring of nutritional status as well as predictors of diseases.[1] Obesity is now the most prevalent form of malnutrition globally. The prevalence of obesity and its associated co-morbid conditions is on the increase worldwide and has emerged as one of the major pressing global health issues in recent decades.[2] A major factor to this is modern diet which is laden with egregious additives to enhance taste. Furthermore, commercially prepared foods are also laden with preservatives to increase shelf lives of foods for bigger profits.[3]

Different diets have been proposed to cut excess calorie and encourage healthy living.[3] Atkins’ diet is low on carbohydrate and was marketed by its founder in the 1970s, with claims that it curbs obesity and consequent chronic diseases such as hypertension associated with it.[4] Similarly, the dietary approach to stop hypertension (DASH) was introduced and subjected to clinical trials 20 years ago. It promotes consumption of beneficial dietary constituents such as fiber, potassium, and magnesium. Hence, the emphasis is on salads, fruits, vegetables, whole grains, and reduced intake of processed meat.[3] A positive anthropometric effect of DASH diet was demonstrated in a study of 44 obese women.[5],[6] A Personalized Food Avoidance Dietary Approach to Stop Hypertension (PFADASH), a modification of DASH, was conceived to address primary and secondary intolerance to immune unfamiliar and egregious food substances to abate immune dysfunction underlying immunomediated inflammatory diseases such as hypertension. The dietary regimen is not calorie-restrictive; hence, its emphasis is on dietary proscription and not prescription (what to avoid and not what to take).[3],[7],[8] PFADASH dietary prescriptions and proscriptions are guidelines for dietetic assessments and counseling of study participants by trained personalized food avoidance counselors and dieticians.

Anthropometric parameters such as body mass index (BMI), abdominal circumference (AC), and triceps skin fold thickness (TSFT) are useful in clinical practice, especially in monitoring the effect of an intervention. We therefore sort to investigate the anthropometric effect of a PFADASH on anthropometric parameters.


  Materials and Methods Top


Study protocol/design

The longitudinal study which was part of an open label randomized controlled trial was carried out at Chiolive International Medical Research Organization (CIMRO), Trans-Ekulu, Enugu, Nigeria. The protocol for the open randomized controlled trial of personalized food avoidance dietary approach to address hypertension was approved by the University of Nigeria Teaching Hospital Ethics Committee (certificate no. NHREC/05/01/2008B-FWA00002458-IRB00002323), issued June 28, 2012, renewed on July 28, 2015 and January 23, 2017. Written informed consent was obtained from each of the research participants.

Anthropometry measurements undertaken by study participants include BMI, AC, and TSFT.

BMI was calculated using each participant’s height (meters) and weight (kg) with the formula kg/m2.

AC was calculated by measuring the distance around the abdomen using navel as the reference point.

TSFT was measured halfway between the olecranon process of the elbow and the acromial process of the scapular using harpers calipers.

Anthropometric parameters were assessed for the study participants every 2–4 weeks.

Intervention and control subjects were compared at 3 monthly time points. Analysis of variance was used in the multivariate comparison of outcome of the two groups.

Trend analysis with graphs was used to analyze continuous data collected over a period of 40–140 weeks.

For the PFADASH intervention, Dietary Compliance Scoring was based on exposure to:

  • a) Primary culprits: amphiphilic fat and oils, glutamatergic flavor enhancers, non-sugar sweeteners;


  • b) Secondary (facultative) culprits (early life unfamiliar, food dislikes, autacoids, modest flavorant, or sweetener content, preservatives, unduly frequent/high dose consumption of normally tolerated foods).


GOOD dietary compliance means that there is established MAJOR (category A) dietary indiscretion less than once a month [OR minor (category B) indiscretion less than once a fortnight]. POOR dietary compliance means that there is established MAJOR dietary indiscretion once a month or more frequently [OR minor indiscretion once a fortnight or more frequently].

Data were analyzed using Statistical Package for Social Sciences version 25 and Microsoft Excel.

Longitudinal trend analysis of anthropometric graph was done with Corel Draw Microsoft software.


  Results Top


[Table 1] presents the results on comparison of TSFT, BMI, and AC between study participants with good and poor compliance.
Table 1: Comparison of TSFT, BMI, and abdominal circumference between study participants with good and poor compliance

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[Table 2] presents the results on relationship between dietary compliance and triceps skinfold, BMI, and AC score of the participants.
Table 2: Relationship between dietary compliance and triceps skinfold, BMI, and abdominal circumference score of the participants

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[Table 3] presents a comparison between the baseline and the last score for those with good dietary compliance and for those with poor compliance
Table 3: Comparison between the baseline and the last score of participants with good dietary compliance and for those with poor compliance

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  Discussion Top


With increasing popularity of lifestyle intervention to address the increasing pandemic of immunomediated inflammatory diseases such as hypertension, diabetes mellitus, and hyperlipidemia which has obesity as often as a co-morbidity, there is growing need to document the anthropometric effect of such interventions.

There was no significant difference for BMI between study participants with good or poor DC to PFADASH. Those with good dietary compliance had higher BMI. For AC, furthermore, there was also no significant difference with higher circumference being associated with good dietary compliance. There was no significant difference between study participants with good and poor dietary compliance for the anthropometric parameter TSFT. However, calculated means indicated those with good dietary compliance to be associated with higher TSFT. There was no significant difference in BMIs of study participants with good or poor dietary compliance. Higher AC of study participants with good dietary compliance highlights that a PFADASH diet is not calorie-restrictive. It is important and interesting to note that inasmuch as a PFADASH does not prescribe calorie restriction like Atkins’ diet or DASH diet which encourages more fruits and vegetables with less carbohydrates, it did not have a negative effect on anthropometric parameters of study participants.

This was a longitudinal study and data were collected over a period of 140 weeks. This enabled multiple datapoints for graph trend analysis, which show anthropometric parameters wax positively and wane negatively in response to exposure to dietary culprits suspected to drive adverse gene expression. This may be in keeping with epigenetics (environmental influence on disease expressions).[3]


  Conclusion Top


Our study has shown that a PFADASH though not calorie-restrictive has no negative impact on anthropometric parameters. Although part of an open clinical trial of a PFDASH, it is a longitudinal study. Hence, it is of limited value for firm recommendations for using the dietary intervention for the management of obesity.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Adejumo EN, Adejumo AO, Azenabor A, Ekun AO, Enitan SS, Adebola OK, et al. Anthropometric parameter that best predict metabolic syndrome in south west Nigeria. Diabetes Metab Syndr 2019;13:48-54.  Back to cited text no. 1
    
2.
Al-Qahtani AM Prevalence and predictors of obesity and overweight among adults visiting primary care settings in the southwestern region, Saudi Arabia. Biomed Res Int 2019;2019:8073057.  Back to cited text no. 2
    
3.
Okafor MT Efficacy of a personalized food avoidance dietary approach to stop hypertension. MD thesis. 2020. University of Nigeria. Department of Pharmacology and Therapeutics.  Back to cited text no. 3
    
4.
Miller BV, Bertino JS, Reed RG, Burrington CM, Davidson LK, Green A, et al. An evaluation of the Atkins’ diet. Metab Syndr Relat Disord 2003;1:299-309.  Back to cited text no. 4
    
5.
Hadi A, Hasani H, Kafeshani M The effect of DASH diet on anthropometric indices and body composition in healthy obese women. Qom Univ Med Sci J 2019;13:40-9.  Back to cited text no. 5
    
6.
Chijioke CP, Okafor MT, Ndiokwelu C, Gbenimachor N, Ironkwe N, Nwosu N, et al. Validation of antihypertensive drug requirement to measure the severity of hypertension and the efficacy of lifestyle intervention. West Afr J Pharmacol Drug Res 2020;34:29-34.  Back to cited text no. 6
    
7.
Chijioke C, Ndiokwelu C, Nwosu N, Okafor M, Nubila I, Chigbo N, et al. Open clinical trial of a personalized food avoidance dietary counselling for the control of essential hypertension. J Hypertens 2016 34:e295.  Back to cited text no. 7
    
8.
Chijioke CP, Okafor MT, Nubila I, Onah I, Chigbo N, Anakwue R, et al. Controlled clinical trial of personalized food avoidance dietary approach to stop hypertension (PFADASH): 2 Years follow-up report. J Hypertens 2018 36:e318-9.  Back to cited text no. 8
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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