alexa LIPID Profile and Growth Indicators among Offspring’s of Diabetic Parents in Karachi, Pakistan | OMICS International
ISSN: 2155-6156
Journal of Diabetes & Metabolism
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

LIPID Profile and Growth Indicators among Offspring?s of Diabetic Parents in Karachi, Pakistan

Meraj Rahim1*, Mariam Rahim2, Masood Anwar Qureshi1, Shaheen Sharafat3, Zaman Shaikh4, Munim Abdul Rahim5 and Mubashir Zafar6

1Department of Physiology Dow University of Health Sciences, Karachi, Pakistan

2Final year MBBS student, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan

3Department of Microbiology, Dow University of Health Sciences, Karachi, Pakistan

4Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan

5Institute of Isharat ul ebad of oral health sciences, Dow university of Health Sciences, Karachi, Pakistan

6School of Public Health, Dow University of Health Sciences, Karachi, Pakistan

*Corresponding Author:
Dr Meraj Rahim
Assistant Professor of Physiology
Department of Physiology
Dow University of Health Sciences, Karachi, Pakistan
Tel: 92-2134014774
E-mail: [email protected]

Received date: June 09, 2014; Accepted date: September 26, 2014; Published date: October 06,2014

Citation: Rahim M, Rahim M, Qureshi MA, Sharafat S, Shaikh Z, et al. (2014) LIPID Profile and Growth Indicators among Offspring’s of Diabetic Parents in Karachi, Pakistan. J Diabetes Metab 5:443 doi: 10.4172/2155-6156.1000443

Copyright:2014 Rahim M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Diabetes & Metabolism

Abstract

Background: Type 2 diabetes mellitus has become a universal problem. Globally 80% of lower and middle income countries are suffering from diabetes. Different studies show that offspring from diabetic parents growth indicators were disturbed the lipid profile and growth indicators. The objective of the study was to determine the levels of lipid profile and growth indicators among offspring of diabetic parents.
Material and methods:
Cross sectional study was done. Total 180 subjects were recruited from Dow university of Health sciences and classified as both parents were diabetic (BDP), Single Parents Diabetic (SDP) and no parents were diabetic (NDP). The Growth indicators lipid profile was measured. The analyst concentration of Lipids was analyzed by The Roche Hitachi Analyzer 902 Automated Analyzer automatically .Fasting blood sugar levels was determined by glucose oxidase.
Results: Those offspring who gave history of diabetes in parents had their growth indicators and Lipid Profile were disturbed or raised when compared with non -diabetic parents. 25.6%, 5.6% and 5.6% of offspring belongs to Both Parents Diabetic (BDP), Single Diabetic Parents (SDP) and Not Diabetic Parents (NDP) respectively have obese. Similar lipid profile indicators also raised as More BDP and SDP subjects had high cholesterol (>200 mg/dL) than NDP (76.9% and 29.6% versus 10% respectively), More BDP and SDP subjects had high cholesterol (>150 mg/ dL) than NDP (23.1% and 2.8% versus 0% respectively). More BDP and SDP subjects had high cholesterol (>130 mg/dL) than NDP (76.9% and 29.6% versus 15% respectively).
Conclusions: Lipid profiles of offspring were related to diabetic parent’s history. Early screening and change in lifestyle modification can be a preventive intervention for the risk of developing diabetes in future.

Keywords

Type 2DM; Family history; Lipid profile

Background

Worldwide, the trend for incidence and prevalence of type 2 diabetes is rapidly increasing and by the year 2030 it is expected that the disease will effect 439 million adults [1-3].A recent cross sectional study was conducted in Pakistan’s rural and urban areas where 5433 individuals were recruited out of which almost 19% of the population showed prevalence of diabetes [4]. Multi system organ damage is seen in subjects with chronically elevated serum glucose levels [4]. New awareness strategies should be developed by the public health sector of the state for an intervention to prevent diabetes in the society. To achieve success in such a strategy, a widely applicable, easily measured index needs to be developed which has a high predictablity.

Various risk factors and predictive models have been suggested. Genetic risk factora and inflammatory biomarkers are a new addition to the traditional risk factors, to allow us to predict diabetes in the population. However, these new novel markers have been reported to be of limited benefit [5-8]. By evaluating risk factors and genetic factors we can predict diabetes, but special biomarkers should be designed for specificity. The independent risk factor for developing type 2 diabetes was high fasting plasma glucose levels, even in subjects who were within euglycemic range [9,10]. Thus risk factors such as estimating fasting plasma glucose levels and deranged lipid profile are key indicators for development of diabetes [11].

Studies comparing monozygotic/dizygotic twins and normal population with family history of type 2 diabetes have shown a preference towards the hereditary basis for insulin resistance and impaired insulin secretion mechanism [11]. Offspring with single parent have a 3-5 time risk of developing DM whereas offspring with both diabetic parents have 6 times the risk for developing DM. Early age for onset of diabetes is noted in offspring with strong family background of diabetes [12]. This study’s main objective was to determine the role of diabetic parentage on lipid profile and growth indicators within their offspring.

Methodology

Study area, design and period, sampling technique, sample size

Participants were inducted from different campuses of Dow University of Health Sciences Karachi, (DUHS) eg National Institute of Diabetes and Endocrinology (NIDE), Dow College of Pharmacy and Institute of Nursing.

Cross-sectional study was conducted and the subjects were classified according to their parent’s family history, All participants who had were defined as NDP (no diabetic parents), SDP (Single diabetic parent) BDP (both diabetic parents).

The study was carried from 2010 to 2011. Non probability convenient samplings were used. Sample size is 180, calculated for a confidence interval of 95% and a 5% margin of error. It was estimated by taking the % frequency of diabetes as 13.5% in our population [13]. In this study the inclusion criteria was adults’ age between 18-24 yrs with no history of medical problems or any recent or remote diseases. All participants who had NDP (no T2DM parents), SDP (Single T2DM parent) BDP (both diabetic parents).Exclusion criteria was subjects with H/O diabetes or any known endocrinopathies. BMI and WHR were calculated by Asian –Pacific cutoffs [14] .The criterion for fasting plasma glucose was <100 mg/dl and impaired fasting glucose was above 125 mg/dl or (5.6-6.9 mmol/l) according to DDRL {Dow Diagnostic Research laboratories}..

Instrument and data collection

Participants were provided with a questionnaire and get their consent as well as the information needed; age, gender, history of diabetes in either or both parents, medical history and personal habits. The participants were instructed to come in fasting condition (12 hrs) to Dow Diagnostic Research Laboratories (DDRL) Ojha Campus. Batches of 20 subjects were inducted each time for anthropometric measurements (height, Wt, Waist-Hip circumference) and blood collection (12 ml). Metabolic and Biochemical parameters were assessed. The Weight was recorded by the Stadiometer. Participant stood on center of platform to distribute their weight otherwise weight data is affected. Height was measured by Stadiometer’s head piece. Waist circumference (cm) was`measured at a level between the lower rib margin and iliac crest with the tape all around the body in horizontal position. Measurer should stand at side of participant. Hip measurement was done at fullest point at buttocks in (cm). Blood pressure was recorded.

For estimation of glucose and lipid profile the blood was centrifuged in HERMLE 2323 centrifuge for 10 minutes and shifted to ROCHE HITACHI 902 AUTOMATED ANALYZER. The working Principle of Hitachi 902 (Photometer) analyzer uses the photometric technique of glucose estimation.

Data management and analysis

Data were entered and analyzed using SPSS16.0, Means, Standard deviation were calculated and means were compared across all three groups using one way anova (ANOVA) after findings of significance values, a multiple comparison between pair of means were made using, Scheffe method of pair wise comparison, a p-value <0.05 was considered as significant as differences in means of two groups. Correlations were measured by Pearson correlation method.

Ethical Consideration

The study was approved by ethical committee of Dow University of Health Sciences Karachi Pakistan.

Results

The BMI for BDP and SDP groups were significantly greater than the NDP group (p<0.05, Table 1).

Parameters BDP
n=39
mean(SD)
SDP
n=71
mean(SD)
NDP
n=70
mean(SD)
Body Weight (Kg) 70.38±10.78 57.27 ± 14.33 57.03 ± 10.17
BMI (Kg/m2) 25.58 ± 5.15 22.26 ± 6.80 21.02 ± 6.19
Systolic Blood Pressure(mmHg) 116.53±5.51 113.52±7.57 113.00±7.29
Diastolic Blood Pressure(mmHg)   80.25±7.42 74.08±5.99 73.85±6.43
  Male Female Male Female Male Female
Waist /Hip Ratio
At High Risk
14.3 % 37.5% 23.1% 30.4% 0.0% 0.0%

Table 1: Physical characteristics of offspring of Both Diabetic (BDP), Single Diabetic(SDP) and Non Diabetic Parents(NDP).

In Figure 1 the frequency of BMI in offspring of BDP, SDP and NDP underweight (BMI<18.5) was 2.6%, 19.7% and 31.4%, in normal weight (BMI, 18.5-22.9) it was 20.5%, 50.7% and 50% and in overweight (BMI ≥ 23) it was 76.9%, 29.6% and 18.6% respectively .BDP offspring showed high WHR for males i.e.14.35%, 37.50% for females, and SDP offspring had 23.1% in males and 30.4% in females, whereas offspring of NDP had a normal WHR(Waist hip ratio) (Table 1). The diastolic BP (mm Hg) was significantly higher among BDP (80.25 ± 7.4; p<0.05) as compared to both SDP (74.08 ± 5.9) and NDP (73.85 ± 6.4), whereas the systolic BP was significantly higher among BDP only when compared to NDP (p<0.05) and not with SDP (Table 4).

diabetes-metabolism-Comparison-BMI

Figure 1: Comparison of BMI between Groups.

Fasting Plasma Glucose (FPG) were in normal range in all three groups but the mean FPG level was higher in BDP as compared to SDP and NDP (p- value 0.05) (Table 2).

Parameters BDP
n=39
Mean (SD)
SDP
n=71
Mean (SD)
NDP
n=70
Mean (SD)
Fasting Plasma Glucose 4.73 ± 0.75 4.65 ± 0.47 3.61 ± 0.24
Serum Cholesterol 146.91±24.51 19.04 ± 14.57 15.55 ± 13.49
Serum Triglycerides 138.84±109.75 77.35±29.87 66.33±29.58
LDL mg/dl 111.28±32.73 103.14±24.17 95.83±29.32
HDL mg/dl 44.48±10.52 46.89±20.48 48.98±8.43
Chol:HDL 4.07±1.35 3.24±0.68 3.45±1.07

Table 2: Biochemical parameters of offspring of Both Diabetic (BDP), Single Diabetic (SDP) and Non Diabetic Parents (NDP).

Using the criterion established by DDRL, 5.1% and 4.2% of BDP and SDP offsprings, respectively, had impaired fasting glucose (Figure 2). According to Table 1 the WHR (Low Risk) for males is ≤ 0.95, and for females it is ≤ 0.80 .WHR (High Risk) is 1.0 in males and 0.85 in females.

diabetes-metabolism-Fasting-Glucose

Figure 2: Comparison of Fasting Glucose between Groups.

In Table 1 the mean values for Serum Cholesterol was 171.61 ± 32.37, 159.38 ± 33.24 and 146.91 ± 24.51 in BDP, SDP and NDP. The mean values for Triglycerides (mg/dl) was in BPD (138.84 ± 109.75) as compared to SDP (75.33 ± 29.58) and NDP (77.35 ± 29.87) showing highly significant difference between values of NDP and BDP (p<0.05) as well as between those of SDP and BDP (p<0.05) but not with NDP and SDP (Table 3).

Parameters NDP VS SDP*
p-value
NDP VS BDP*
p-value
SDP VS BDP*
p-value
Body Weight (Kg) 0.994 0.00 0.00
BMI (Kg/m2) 0.537 0.005 0.056
FPG (mmol/l) 0.701 0.451 0.874
Serum Cholesterol mg/dl 0.05 0.00 0.125
Serum Triglycerides mg/dl 0.978 0.000 0.000
LDL 0.13 0.356 0.025
Chol :HDL 0.495 0.000 0.000

Table 3: Sheffe’ multiple pair wise comparison of physical and biochemical parameters.

Table 2 shows that mean values for the LDL were significantly raised in BDP and SDP as compared to NDP.

In Table 3 the serum cholesterol p-value was <0.05 in NDP versus BDP and it was also <0.05 in SDP versus BDP. HDL was lower in BDP as compared to SDP and NDP. Serum cholesterol-HDL’s P- value had a statistical significant difference<0.05 in BPD and SDP.

In Table 4 shows intra group relationship of physical and biochemical parameters. Chlolestrol HDl ratio and TG and HDL ratio were significantly differ between SDP, BDP versus NDP.

Groups NDPSDP BDP
  r P value r P value r P value
BMI-HDL Ratio 0.099 0.437 0.37 .002<0.05 0.11 0.952
Chol-HDL Ratio 0.193 0.110 0.685 .00<0.05 0.488 0.002
TG-HDL Ratio 0.292 .014<0.05 0.57 .00<0.05 0.635 0.00

Table 4: Intragroup correlation (Pearson) (r and P values) of physical and biochemical parameters.

Serum Cholesterol level was 25.65 % in BDP while in SDP it is 5.6% (Figure 3). Serum Triglycerides>150mg/dl was found to be 23 % in BDP and 2.8% in SDP (Figure 3). LDL>130 mg/dl was found in 23.1%in BDP and 8.5% in SDP (Figure 5).

diabetes-metabolism-Cholesterol-Groups

Figure 3: Comparison of Cholesterol between Groups.

diabetes-metabolism-HDL-Groups

Figure 5: Comparisons for HDL between Groups.

Discussion

The study revealed that offspring of diabetic parents with raised disturbed growth indicators, Biochemical parameters such as Serum Cholesterol, Triglycerides also showed raised levels thus suggesting that they are the predicting factors for the onset of diabetes .

In a recent study in 2013 also evaluated the these predicting factors and they also found the levels raised in all the subjects lipid profile [15]. In this present study the waist and hip circumference and its ratio in offspring of both diabetes was also increased suggesting that there is metabolic disorders. WHR in addition to BMI has also been shown as the measure of obesity. It is however, increasingly known that for a given BMI, central rather than lower body fat distribution, leads to greater risk of metabolic and cardiovascular complications of obesity [16].

Schmidt et al. [17] also analyzed the ratio between waist and hip and considered it very crucial. Cassano et al. [18] also proved that abdominal fat was important in predicting onset of diabetes risk. Wei et al. [19] also confirmed importance of abdominal fat and waist circumference. The offsprings in my present study with presented with raised anthropometric parameters. Further, insulin resistance which may be a genetically inherited trait [20] is also known to enhance lipolytic activity thereby increasing fatty acid levels thus bringing about these altered changes in lipid profile and can also cause dyslipidemia in individuals with normal glucose tolerance [21].

In the present study mean values were markedly lower for HDL and higher for LDL in the groups (BDP and SDP) having family history of diabetes when compared to other group (NDP). It was also assessed that those with positive family history of diabetes showed significant p-value for serum cholesterol and LDL as compared to those who do not have diabetes in their families. Subjects with T2DM are refractory to insulin-stimulated glucose uptake by the target cells [22,23] and also prone to dyslipidemia especially increased triglyceride and decreased high-density lipoprotein (HDL) levels, hypertension and ischemic heart disease [24,25] (Figure 4). Body fat mass supply and deposition are due to multiple environmental and genetic factors. Obesity is linked with insulin resistance, hyperinsulinemia, and incident T2DM [26,27]. Furthermore insulin has an anabolic special effect on fat metabolism leading to fat deposition and obesity [28].

diabetes-metabolism-Triglyceride-Groups

Figure 4: Comparison of Triglyceride between Groups.

Impaired Glucose Tolerance (IGT) was recognized in children, in the group with positive history of diabetes in their families because obesity in childhood has noteworthy increased in recent years [29,30] and it is strongly related with insulin resistance, the main public health policies are centered on screening obese children and youths. Weijnen et al. [31] researched that levels of insulin & lipid profile were more in those who gave a family history of diabetes & later development of obesity [31].

Conclusion

Hyperlipidemia and anthropometric measurements was found to be raised in those who give history of diabetes in their parents suggesting that these parameters may be the factors for development of diabetes in future.

Acknowledgements

I am grateful to Dow University of Health sciences for the grant [Reference number DUHS/DR/2010/485] and support for research, and also giving me the permission to carry out my study among students of the campus who happen to be of same age groups required in my study.

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

  • International Conference on Internal Medicine May 21-22, 2018 Osaka, Japan
    May 21-22, 2018 Osaka, Japan
  • International conference on Fitness and Expo June 04-05, 2018 Baltimore, Maryland, USA
    June 4-5, 2018 Baltimore, USA
  • Annual Congress on Research and Innovations in Medicine July 02-03, 2018 Bangkok, Thailand
    July 02-03, 2018 Bangkok, Thailand
  • International Conference on Medical and Health Science August 24-25, 2018 Tokyo, JAPAN
    August 24-25, 2018 Tokyo, Japan

Article Usage

  • Total views: 11710
  • [From(publication date):
    October-2014 - Dec 13, 2017]
  • Breakdown by view type
  • HTML page views : 7967
  • PDF downloads : 3743
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version