Evidence map›Paper›PMID 31993391›Full record

ArticleIranian journal of public health2019

Body Mass Index versus Other Adiposity Traits: Best Predictor of Cardiometabolic Risk.

Muhammad Saqlain, Zainab Akhtar, Raheela Karamat, Samra Munawar, Maria Iqbal, Muhammad Fiaz, Muhammad Mubeen Zafar, Sadia Saeed, Muhammad Farooq Nasir, S M Saqlan Naqvi and 1 more

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Article in Iranian journal of public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Muhammad SaqlainDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Zainab AkhtarDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Raheela KaramatDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Samra MunawarDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Maria IqbalDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Muhammad FiazDepartment of Pathology, Shaheed Zulfiqar Ali Bhutto Medical University, Islamabad, Pakistan.
Muhammad Mubeen ZafarDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Sadia SaeedDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Muhammad Farooq NasirDepartment of Entomology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
S M Saqlan NaqviDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Ghazala Kaukab RajaDepartment of Biochemistry, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA number of anthropometric indices have been used in different world populations as markers to estimate obesity and its related health risks. The present study is large population based study dealing with five anthropometric obesity scales; Body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR), basal adiposity index (BAI), and Visceral adiposity index (VAI) to identify common adiposity trait(s) that best predict obesity and associated health complication(s).

methodsA total of 4000 subjects including 1000 in each category of BMI from four provinces (Punjab, Sindh, Kahyber pakhtoonkha and Balochistan) of Pakistan from 2012-2017 were collected. Complete anthropometric measurementswere obtained and blood samples were collected and Biochemical profiling was performed. Descriptive statistics, linear regression, binary and multiple regression analysis was done.

resultsOur data analysis explored the relationships of obesity five indices; BMI, WC, WHR, BAI, and VAI with common metabolic health complications. Effect size analysis clearly indicates that a unit increase in BMI significant raised all anthropometric and clinical parameters. General and sex specific association analysis of adiposity traits with risk phenotypes (hypertension, hyperglycemia and dyslipidemia) indicated significant associations of WC with all three metabolic risks. Varying degrees of correlations of other adiposity traits with metabolic risks were observed. Frequency of different obesity classes among obese population group were as follows; 55.7% class I, 28.50% Class II and 15.80% Class III.

conclusionWC is the strong predictor of obesity associated metabolic health issues in Pakistani populations. While BMI has significant increasing effect on other obesity indices like WHR, VAI and BAI.

Indexed as

Body mass indexObesityPakistanWaist circumference

Identifiers

PMID31993391
PMCPMC6974848

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.