Journal of Clinical and Diagnostic Research, ISSN - 0973 - 709X

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Thanking you
With sincere regards
Dr. Rajendra Kumar Ghritlaharey, M.S., M. Ch., FAIS
Associate Professor,
Department of Paediatric Surgery, Gandhi Medical College & Associated
Kamla Nehru & Hamidia Hospitals Bhopal, Madhya Pradesh 462 001 (India)
On May 11,2011

Dr. Shankar P.R.

"On looking back through my Gmail archives after being requested by the journal to write a short editorial about my experiences of publishing with the Journal of Clinical and Diagnostic Research (JCDR), I came across an e-mail from Dr. Hemant Jain, Editor, in March 2007, which introduced the new electronic journal. The main features of the journal which were outlined in the e-mail were extensive author support, cash rewards, the peer review process, and other salient features of the journal.
Over a span of over four years, we (I and my colleagues) have published around 25 articles in the journal. In this editorial, I plan to briefly discuss my experiences of publishing with JCDR and the strengths of the journal and to finally address the areas for improvement.
My experiences of publishing with JCDR: Overall, my experiences of publishing withJCDR have been positive. The best point about the journal is that it responds to queries from the author. This may seem to be simple and not too much to ask for, but unfortunately, many journals in the subcontinent and from many developing countries do not respond or they respond with a long delay to the queries from the authors 1. The reasons could be many, including lack of optimal secretarial and other support. Another problem with many journals is the slowness of the review process. Editorial processing and peer review can take anywhere between a year to two years with some journals. Also, some journals do not keep the contributors informed about the progress of the review process. Due to the long review process, the articles can lose their relevance and topicality. A major benefit with JCDR is the timeliness and promptness of its response. In Dr Jain's e-mail which was sent to me in 2007, before the introduction of the Pre-publishing system, he had stated that he had received my submission and that he would get back to me within seven days and he did!
Most of the manuscripts are published within 3 to 4 months of their submission if they are found to be suitable after the review process. JCDR is published bimonthly and the accepted articles were usually published in the next issue. Recently, due to the increased volume of the submissions, the review process has become slower and it ?? Section can take from 4 to 6 months for the articles to be reviewed. The journal has an extensive author support system and it has recently introduced a paid expedited review process. The journal also mentions the average time for processing the manuscript under different submission systems - regular submission and expedited review.
Strengths of the journal: The journal has an online first facility in which the accepted manuscripts may be published on the website before being included in a regular issue of the journal. This cuts down the time between their acceptance and the publication. The journal is indexed in many databases, though not in PubMed. The editorial board should now take steps to index the journal in PubMed. The journal has a system of notifying readers through e-mail when a new issue is released. Also, the articles are available in both the HTML and the PDF formats. I especially like the new and colorful page format of the journal. Also, the access statistics of the articles are available. The prepublication and the manuscript tracking system are also helpful for the authors.
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Overall, the publishing process with JCDR has been smooth, quick and relatively hassle free and I can recommend other authors to consider the journal as an outlet for their work."

Dr. P. Ravi Shankar
KIST Medical College, P.O. Box 14142, Kathmandu, Nepal.
On April 2011

Dear team JCDR, I would like to thank you for the very professional and polite service provided by everyone at JCDR. While i have been in the field of writing and editing for sometime, this has been my first attempt in publishing a scientific paper.Thank you for hand-holding me through the process.

Dr. Anuradha
On Jan 2020

Important Notice

Original article / research
Year : 2010 | Month : December | Volume : 4 | Issue : 6 | Page : 3421 - 3424

A Study Of Body Mass Index In Healthy Individuals And Its Relationship With Fasting Blood Sugar


*MD Biochemistry, **MD Community medicine, ***MD Pharmacology

Correspondence Address :
Dr. Vittal BG,
Assistant Professor,
Department of Biochemistry,
Government Medical College,
Hassan-573 201,
Mobile No: 09141400766


Background and objectives: Obesity is a global epidemic and is on the rise. It is defined as a body mass index (BMI) which is equal to or more than 30. It is one of the modifiable risk factors of type 2 diabetes. This study was undertaken to assess the association between BMI and fasting blood sugar (FBS) and also to verify whether BMI increases with age.

Subjects and Methods: This prospective study included 400 healthy adult individuals who met the inclusion criteria. Fasting blood samples were collected to measure FBS by the glucose oxidase method. Thirty individuals were excluded from the study, as their blood glucose levels were in the diabetic range.

Results: Among 370 individuals, a positive correlation was observed (Pearson’s correlation coefficient r = + 0.26) between BMI and FBS. There was a stepwise increase in the magnitude of BMI with an increase in age in decades. Although the increase in mean FBS was observed with age, a statistically significant (p = 0.00093) increase in mean FBS was observed only in the 4th decade of life.

Interpretation and conclusions: The observed positive correlation between BMI and FBS reiterates the diabetogenic effect of adipose tissue and emphasizes the importance of the maintenance of normal BMI to prevent the early onset of diabetes.


Obesity, Body mass index, Fasting blood sugar.

Body mass index (BMI) is a good measure of general adiposity. It is defined as the weight in kilograms, divided by the square of the height in meters (kg/m2).(1) A person can be categorized as underweight if his/her BMI is ≤18.5, as normal weight if his/her BMI is in the range of 18.5–24.9, as overweight if his/her BMI is between 25 to 29.9 and as obese if his/her BMI is ≥30.(2) A raised BMI value is an established risk factor for ischaemic heart disease, stroke and carcinomas.(3)

Obesity is one of the most important modifiable risk factors in the pathogenesis of type 2 diabetes. The mechanism by which obesity induces insulin resistance is poorly understood. Adipocytes secrete a number of biological products (leptin, TNF-alfa, free fatty acids, resistin, and adiponectin) that modulate insulin secretion, insulin action and body weight and may contribute to insulin resistance.(4) A positive correlation is assumed to exist between BMI and fasting blood sugar (FBS) levels.

Global epidemic obesity - "globesity" - is rapidly becoming a major public health problem in the world and is on the rise. In many populations, the average BMI has been rising by a few percent per decade, thus fuelling the concern about the effects of increased adiposity on health.(5)

This study was undertaken to determine the correlation between FBS and BMI in an adult healthy Indian population and also to check whether BMI increases with age.

Material and Methods

This prospective study was conducted at Sri Chamarajendra Hospital, a teaching hospital which is affiliated to the Hassan Institute of Medical Sciences, during May – August 2010.

After obtaining permission from the Institutional Ethical Committee, normal healthy individuals attending the hospital for routine health check-up were included in the study. Paediatric, pregnant, psychiatric and diabetic subjects were excluded from the study. The study group included 400 persons of the age group of 21-60 years. After obtaining the informed consent, the age, sex, height and weight of the subjects were recorded. Weight was recorded to nearest 0.5 kg and height was recorded to nearest 0.5 cm. Fasting (8-12 hours of overnight fasting) venous blood samples were collected by venipuncture of the median cubital vein in a vacutainer and were centrifuged to separate the plasma. Fasting plasma glucose levels were estimated by the Glucose oxidase method by using an ERBA-Transasia fully automated analyser.

The BMI for each subject was calculated by using the standard formula i.e., weight in kilograms divided by height in square meters.1 All the variables including age, sex, height, weight, BMI and FBS from study group were tabulated and analysed statistically. Pearson’s correlation coefficient was used to find the correlation between FBS and BMI. Student’s t-test was used to check the statistical significance of the changes in BMI and FBS with respect to age.


Four hundred apparently healthy subjects who met the inclusion and the exclusion criteria were included in the study. Thirty members were excluded from the study population as their fasting blood glucose levels were in the diabetic range. Of the 370 members, 289 (78.1%) were men and 81 (21.9%) were women. The study included subjects who were in the age group of 21 to 60 years. The mean age of the male subjects was 37.98 ± 10.51 years and the mean age of the female subjects was 36.4 ± 10.11 years.

(Table/Fig 1): Age and sex distribution of study population
The mean FBS of the study population was 90.70 ± 10.71 mg/dl and the mean BMI was 25.1 ± 3.38. The Pearson’s correlation coefficient between FBS and BMI of the study population was positive (r = + 0.26).

(Table/Fig 2): Mean BMI and mean FBS of different age groups
There was a stepwise increase in the magnitude of BMI, with an increase in age in decades. Although an increase in the mean FBS was observed over decades, a statistically significant increase in mean FBS was observed (p = 0.00093) only in the 4th decade of life i.e., as the age group increased from the 3rd to the 4th decade of life.


In the present study, BMI showed a positive correlation with FBS (Pearson’s correlation coefficient r = + 0.26). A positive correlation between BMI and blood sugar was also reported by other studies.(6),(7) Ethnicity affects the association between obesity and diabetes and that probably explains the different levels of association between obesity and blood glucose levels which are observed in various studies.(8)

The mean BMI of different age groups showed an increasing trend over the decades and an increase in mean BMI was found to be more marked from the 3rd to the 4th decade. The prevalence of obesity, as measured by BMI, is high in many countries all over the world and is rising. It is mainly attributed to the changing lifestyles and dietary habits.(5),(9)

Mean FBS increased with increasing age and with increasing BMI. Significant increase in mean FBS was observed during the 4th decade of life.

The mechanism by which obesity induces insulin resistance is poorly understood, but a number of mechanisms have been suspected to be involved. Obesity causes peripheral resistance to insulin-mediated glucose uptake and may also decrease the sensitivity of the beta-cells to glucose.(10) These changes are largely reversed by weight loss, leading to a fall in blood glucose concentrations towards normal levels. Weight gain precedes the onset of diabetes; conversely, weight loss is associated with a decreased risk of type 2 diabetes. (11),(12)

The administration of resistin, an adipocyte derived hormone, decreases while the neutralization of resistin increases insulin-mediated glucose uptake by the adipocytes. Thus, resistin may be a hormone that links obesity to diabetes.(4) Leptin is produced by adipocytes and is secreted in proportion to the adipocyte mass. It signals the hypothalamus about the quantity of stored fat. Studies in humans and animals have shown that leptin is associated with obesity and insulin resistance.(13) The deficiency of adiponectin, an adipocyte-derived hormone, plays a role in the development of insulin resistance and subsequently, type 2 diabetes.(14)

Retinol-binding protein 4, free fatty acids, tumour necrosis factor-alpha, plasminogen activator inhibitor 1, interleukin-1 beta, uncoupling protein 2 and obestatin are also implicated in the adipose tissue induced pathogenesis of type 2 diabetes.(15)

BMI is a good measure of adiposity; however, the relationship between actual body fat and BMI differs between ethnic groups, and as a consequence, the cut off points for the overweight status and obesity based on BMI, will have to be ethnicity specific.(16)


The observed positive correlation between BMI and FBS reiterates the effect of adipose tissue in impairing blood glucose regulation and emphasizes the importance of the maintenance of normal BMI.

The effects of increasing obesity, as indicated by an increase in BMI over the decades in a population can be disastrous, as it can lead to enormous health costs. Hence, awareness needs to be created in children right from the school age, as well as amongst the parents of these children, in order to have an appreciable impact in preventing or delaying the onset of type-2 diabetes in later life.

Limitations of the study:
Our study did not take into account the other indices of obesity like waist hip ratio and abdominal circumference.

We recommend that further studies must be carried out on a larger sample size with the measurement of waist hip ratio and abdominal circumference as comparative indicators.


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Diaz VA, Mainous AG, Baker R, Carnemolla M, Majeed A. How does ethnicity affect the association between obesity and diabetes? Diabet Med. 2007; 24(11): 1199-204.
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