Risk factors for Type 2 Diabetes Mellitus : Metabolic Syndrome, Insulin Resistance and Primary Prevention

Abstract

Aims: The purpose of the study was to identify the extent of metabolic syndrome on the basis of ATP III and IDF definition in subjects aged 25 years and above from an urban population of Karachi. Also to see the association of risk factors related to diabetes and metabolic syndrome in this population. And finally to prove the hypothesis of intervention effect on the onset of type 2 diabetes in a high risk urban population and evaluate the rate of conversion of IGT to diabetes by these interventions in this population.

Methods: The epidemiological survey was designed to see the prevalence of metabolic syndrome and its risk factors among 500 randomly selected households in Lyari, an urban area within Karachi city in 2004 . We generated a computerized random sample of the households from among the 85,520 households in Lyari Town. There were 11 union councils in Lyari Town where the samples were taken and each union council had equal opportunity to be represented in the sample selection. We expected approximately 1000 adult men and women 25 years and above in the households selected in Lyari Town. If members of a household that had been selected refused to consent to household interviews, we knocked on the third door to the right of that house and seeked consent there. Out of the 85,520 households, 532 households were randomly selected and 867 adults 􀑈25 years old consented to take part in the survey; 363 of these subjects gave blood samples. The second study was a primary prevention trial which was started in the city of Karachi in 2006. It was a prospective randomized clinical trial (RCT) to assess the effect of intervention for 18 months on high risk subjects. Nearly 2000 suspected high risk cases identified by a questionnaire were to be invited to participate. Considering 30% IGT cases in high risk individuals around 600 were expected to have IGT based on OGTT. The IGT cases were then randomly allocated on three different arms, two preventive groups and one control group, all with 200 participants (one of the preventive arms included metformin 500mg twice daily). An estimated 5000 people attended the diabetes prevention lectures and visited the screening camps and around 2300 people filled in the high risk questionnaire only 1825 were identified as high risk. Of these 1739 high risk subjects undertook a standardized oral glucose tolerance test (OGTT) and 317 subjects were identified as having impaired glucose tolerance (IGT group) and were randomized into the three groups.

Results: The prevalence of diabetes was 9.4%, whereas 5.6% had impaired fasting glucose (abnormal glucose tolerance 15%). The prevalence of metabolic syndrome according to the IDF definition and modified ATP III criteria was 34.8% and 49%, respectively. Inclusion of modified waist circumference and specific body mass index (BMI) cut offs for Asians might have helped in this increased prevalence of the metabolic syndrome. Dietary data about specific food items was available for 867 adults. Participants also completed a health and lifestyle questionnaire and 363 subjects provided fasting blood samples for glucose and lipids. Dietary intake was assessed by a questionnaire to identify consumption of 33 specific food items and the dietary patterns categorized into 6 food groups was assessed by cluster analysis. Five dietary patterns were identified through cluster analysis. Cluster 1 had the lowest proportion of persons with metabolic syndrome i.e. 42.7% while cluster 2 had the highest percentage of metabolic syndrome subjects (56.3%) (p=0.09). Consumption of fat and calorie dense foods was significantly higher among highest risk group (cluster 2) compared to lowest risk group (cluster 1) (p = 0.0001). The consumption of food groups containing fruit, milk and meat was also more than twice in high risk compared to low risk group (p = 0.0001). Even within the same population there are marked differences in dietary patterns and these apparently contribute to the risk of developing metabolic syndrome. Insulin Resistance (IR) was defined at 75th percentile cut off of insulin levels (9.25 U/mL) and HOMA-IR (1.82). The 25th percentile cut off was used for defining IR in QUICKI (0.347) and McAuley Index (6.77). In the second study which was the primary prevention trial 273 subjects out of 317 subjects completed the study giving a compliance rate of 86%. A total of 47 incident cases of diabetes were diagnosed during the study. The overall incidence of diabetes was 4 cases per 1000 person-months with the incidence of diabetes as 8.6 cases in the control group, 2.5 cases in the Life Style Modification (LSM) group and 2.3 cases per 1000 person-months in the LSM+drug group.

Conclusion: In the first study we observed high prevalence of metabolic syndrome irrespective of the definition applied in this urban population. This may call for immediate action such as preventive measures to halt the accelerating risk of diabetes and CVD which is leading to a possible unparalleled rise in the cost of health care and human suffering. To initiate a preventive program we need to make dietary changes within the population and we found marked differences in dietary patterns which were apparently contribute to the risk of developing metabolic syndrome in the same population. Dietary pattern studies will help elucidate links between diet and disease and contribute to developing healthy eating guidelines. A common approach towards managing subjects with metabolic risk factors which could help physicians would be able to identify IR cases earlier and defining IR reference values identified from simple indirect methods would be of value in such cases. However larger population based studies are needed to further define and validate the cutoff values defined for insulin resistance in our population. The primary prevention study was initiated after we had some baseline information from our first epidemiological study and it showed that lifestyle intervention had a major impact in preventing diabetes among IGT subjects in this region. However, addition of drug in the intervention did not show any improved results. Resource constrain societies are challenged with the additional burden of diabetes cost on their already ailing economy and such lifestyle intervention approach would be of benefit in such communities. Therefore, we recommend that lifestyle modification advice and follow-up should be incorporated in primary health care.