Family History of diabetes

factors associated with impaired cardiac function and car- diac remodeling as suggested by the DCCT/EDIC study [59]. In this study, there was an association between the mean glycated hemoglobin over the entire follow-up period and parameters of cardiac function and structures evaluated by cardiac magnetic resonance. However, this study failed to find any difference between previously in- tensive versus conventionally treated patients. In a review of CVD in type 1 diabetes the authors state that in the past 40 years, there was a 70% reduction in CVD mortality in type 1 diabetes presumably because of the progress in CVD risk management. However, CVD mortality may increase approximately 50% for each 1% increase in HbA1c between the ages of 45 and 64 years [44]. The difficulty in identifying those patients with high CVD risk was illustrated by a recent case report of a 48- years-old woman with a long duration of type 1 diabetes but without evidence of microvascular complication, traditional CVD risk factors and a family history of dia- betes. She had a rapidly progressive severe coronary atherosclerosis and experienced a myocardial infarction by the age of 42 [60].

Most cases of type 1 diabetes occur sporadically in the absence of family history of diabetes [28]. The empiric risk of being affected if a first-degree relative has dia- betes is 5% [29]. Although more than 85% of patients with type 1 diabetes lack a positive family history, a high familial clustering with a mean prevalence of 6% in sib- lings is usually found [30]. In our study, the frequency of first-degree relatives with either types of diabetes was 10.8% in the whole cohort, and was significantly higher in patients with negative autoantibodies (24.1% vs. 9.4%, p < 0.001), possibly indicating different pathways of dis- ease inheritance.

A total of 6,700 deliveries were conducted during the study period at the Department of Obstetrics and Gynecology, AMPH, providing 159 GDM mothers with newborn babies and 130 controls. The comparison of the maternal demographic characteristics of women with and without GDM is shown in Table 1. The majority of the cases and controls belonged to the 25–34 years age group (48% and 55%, respectively). The χ 2 test revealed a highly statistically significant association between age and GDM status, with standardized residuals analysis confirming that there were more GDM mothers in the older age group (z=2.0) and few controls in that group (z = -2.2). Significantly more control case mothers were in the younger age group (z = 2.1). The most frequent parity history was 2–4 deliveries (72, 45%; and 72, 55%, respectively). Incidence rates of cesarean delivery and assisted vaginal delivery were statistically higher in the GDM group than in the non-GDM group (P=.012). Clinical risk factors for GDM are compared in Table 2. There was a strong association between BMI category and GDM status, P<0.001. Standardized residuals were all ≥2.9, confirming that a higher proportion of GDM mothers were in the overweight or obese categories. Hypertension, family history of diabetes mellitus, and macrosomia were also significantly more prevalent among pregnant women with GDM.

One of the limitations of this study is that medical charts were not abstracted for 23% of the patients. Health Insurance Portability and Accountability Act pri- vacy laws implemented during the course of the study prevented medical chart abstraction without consent at some SEARCH clinical sites. Patients with abstraction of the medical chart were younger and included a higher proportion of non-Hispanic whites and Hispanics and clinical diagnosis of type 2 diabetes compared with those whose records were not available, potentially leading to some overestimation of the prevalence rates. On the other hand, we may have slightly underestimated rates of DKA because of lower availability of medical charts data at the time of diagnosis for patients with lower family income or Medicaid insurance. Information con- cerning health insurance, family income, parental edu- cation, and family history of diabetes was available only for 53% of all of the patients registered with diabetes (n ⫽ 3666), and more in-depth analyses of these factors need to be deferred to future studies.

(Table 1). At the PPCC, family histories are recorded by clinicians as unstructured free text in the EHR. Free-text fields were transferred into a spreadsheet, where the data were searched by using the string search terms “dm,” “di,” and “dai.” Family history fields containing any of those strings were reviewed by a physician (S.G.A.) for the presence of any family history of diabe- tes. Records with a family history of T2DM or an un- specified history of diabetes were coded as positive for a family history of T2DM. Patients with a family history of diabetes in distant relatives (not first or second degree) were coded as negative for a family history of T2DM.

A total of 301 mothers participated in the study. Their mean age was 22.19±3.3 years. Majority of them that is 54 were of 20 years old. 40% were less than 20 years of age. Among the participants 3.7% were illiterate, 12.3% had studied up to primary, 44.2% had studied up to high school, 30.2% up to pre-university and 9.6% graduates. Among 301 participants 67.8% of them had BPL (Below poverty line) cards and 8.3% had health insurance. They stayed at a mean distance of 3.9km around primary health centre. Consanguineous marriage was seen in 24% of participants. Among these 301 antenatal mothers 60% were in first trimester and 55% were primigravida, 35% were second gravid and 9% were pregnant for more than 2 times. Bad obstetric history was seen in 7% of them. Among the mothers 3.3% had family history of diabetes mellitus and 1 mother had history of gestational diabetes in her mother. Their mean BMI was 21±4 with 26% being low weight, 48.55% normal, 10.3% overweight and 14.6% obese. Mean blood pressure was 108±10 mm of Hg systolic and 68±8 mm of Hg diastolic.

There is considerable evidence suggesting that lifestyle interventions targeting modifiable risk factors can either prevent or delay the onset of Type 2 diabetes 23 . It was found that Type 2 diabetes was significantly reduced with moderate lifestyle interventions in high risk populations 24 . Several recent major clinical trials confirmed that type 2 diabetes can be delayed or prevented in people at high risk by multi component lifestyle modification and it could reduce the incidence of diabetes up to 58% 25 . Evidence demonstrates that change in diet and physical activity can prevent or delay diabetes and its complications 26, 27, 28 People in many parts of the world who follow traditional diet high in fiber have a low risk of Type 2 diabetes 29 . Diets high in fat especially saturated fat worsen glucose tolerance and increase the risk of Type 2 diabetes 30-33 . It is estimated that >90% cases of Type 2 diabetes could be prevented by prudent diet (high in cereal firer and poly unsaturated fatty acids and low in trans-fatty acids and glycemic load), avoidance of overweight and obesity (BMI <25 kg/m 2 ), regular moderate or vigorous physical activity for at least 0.5 hr/d and non- smoking 34 . Regular physical activity decreases the risk of developing Type 2 diabetes among the individuals with family history of diabetes.

accordance to the previous studies the present study also got a statistically significant (p=0.0001) link between GDM and family history. The present study even investigated the role of maternal blood group and fetal sex in the emergence of GDM and found no association. The role of maternal blood group in GDM was studied previously and the result was in accordance to ours. 23 The consumption of non vegetarian diet is found to be a risk factor for GDM, but in our study all the participants except one were non vegetarians. 26 It signifies that BMI of the mother should be considered relevant than the diet. This study would be helpful to increase the public awareness regarding the role of socio-demographic and obstetric factors in the development of GDM. The study reveals that the socio demographic factors like age, religion, family history of Diabetes mellitus and obstetric determinants like previous history of abortion, maternal height, maternal BMI and birth weight of babies influence the occurrence of GDM.

Collected data was analyzed using IBM SPSS Statistics (Version 19) and OpenEpi (Version 3.01). P values < 0.05 were considered statistically significant. In the asso- ciation between diabetes and pesticides exposure, odds ratio (OR) was calculated for each group of pesticides (insecticides, herbicides, fungicides, rodenticides, and molluscicides) and 35 individual compounds. Both crude and adjusted ORs with 95% confidence intervals (CIs) were presented. Adjusted ORs were analyzed using multiple logistic regression controlled for gender, age (continuous), BMI (continuous), cigarette smoking (packs/year), alcohol consumption (glass/week), family history of diabetes (yes, no), and occupation (farmer, non-farmer, other). The control variables were those with difference between cases and controls in addition to the fundamental confounding factors.

The risk of becoming a diabetic for an individual with a positive family history of diabetes increases by two- to fourfold. Offspring’ with a positive family history of diabetes have higher body mass index (BMI) than controls 3 .Obesity and body fat distribution 4 . lifestyle, 5 . impaired glucose tolerance (IGT), and a family history of type-2 Diabetes Mellitus 6,7 .represent risk factors for type- 2 Diabetes Mellitus. First-degree relatives of patients with type-2 Diabetes Mellitus frequently show abnormal glucose tolerance and share several metabolic abnormalities of the full blown disease and have a 30%–40% risk of developing type-2 Diabetes Mellitus themselves 8.

Participants completed a baseline questionnaire includ- ing demographic and clinical characteristics such as age, sex, marital status, education level, medication use, smoking, prevalent cardiovascular disease (CVD), and family history of diabetes mellitus (FH-DM). Waist cir- cumference (WC), weight, and height were measured based on the standard protocols [16], and body mass index (BMI) was calculated as weight (kilogram) divided by square of height (square meters). Systolic (SBP) and diastolic blood pressure (DBP) were obtained by the mean of two measurements taken on the right arm at an interval of 5 min. The participants’ blood samples were collected after an overnight fast of 12–14 h to assess the fasting plasma glucose (FPG), 2 h post load plasma glucose (2 h-PLPG), triglyceride (TG) and high-density cholesterol (HDL-C) level [16]. Triglycer- ide to high-density lipoprotein cholesterol ratio (TG/ HDL-C) was calculated by dividing TG to HDL-C. Physical activity level (PAL) was assessed using the Lipid Research Clinic questionnaire in the first phase of the study. In the second phase, the Modifiable Ac- tivity Questionnaire was used to measure three forms of activities including leisure time, job, and household activities in the past year [18].

As the pregnancy proceeds, insulin resistance is grad- ually increased, especially in the third trimester of the gestational period [6]. Obesity and insulin-resistance re- duce the functionality of β-cells and induce inflamma- tion thought to play key roles in the development of GDM [7]. In this circumstance, early management of GDM which is of immense importance includes medical nutrition therapy (MNT), self-blood glucose monitoring, physical activity, and regular consumption of medica- tions to control hyperglycemia [8]. Approximately 20– 60% of GDM women need pharmacological treatments to control their blood sugar [9]. Insulin is considered a safe and effective medication for women with GDM who failed to respond to medical nutrition therapy [8]. Ad- equate and accurate control of blood glucose can attenu- ate adverse maternal and perinatal outcomes [10]. However, factors predicting antenatal insulin require- ment in women with GDM have not been fully under- stood. Some risk factors, such as advanced maternal age, family history of diabetes, pre-gestational obesity, high oral glucose tolerance test (OGTT) values, history of GDM or poor pregnancy consequences, and polycystic ovary syndrome (PCOS) have been previously addressed in the literature [11, 12].

A cross-sectional study was carried out on elderly (60 years and above) in the field practice area of urban training health center, Tripuri attached to the Department of Community Medicine, Government Medical College, Patiala during the period of April 2015 to March 2016. This study was intended to measure prevalence of diabetes mellitus and associated risk factors. The target population was all the elderly people aged 60 years and above residing in the area of survey for at least six months or more. Then the participants were approached by house to house visits by the researcher and MPW (F). Detailed demographic profile along with anthropometric measurements, age, sex, address, education and occupation, marital status, socio-economic status, Diet, history of diabetes and hypertension, family history of diabetes and/or hypertension, smoking history, alcohol history were taken.

Statistical analyses were performed on SAS V.9.3 (SAS Institute). A two-sided p value less than 0.05 was consid- ered statistically significant. Data from descriptive anal- yses were reported as mean (SD) or median (IQR), or proportions. Participants were stratified into six baseline age groups with 10-year increments starting from 20 to 30 years old to over 70 years old. Linear regression models were performed to assess the relationship between BMI, metabolic parameters, lifestyles and family history of diabetes with age. Diabetes incidence was calculated in each and in the total of all age groups, and age-stan- dardised diabetes incidence was adjusted to the Chinese population in 2010. 15

A detailed family history (Fig. 2) revealed his older brother, IV.1, had diabetes mellitus due to pancreatic hypoplasia, truncus arteriosus, bicuspid aortic valve, hypospadias and umbilical hernia. Their deceased father, III.3, was reported to have a similar physical appearance to the proband, as well as atrial septal defect, umbilical hernia and a positive result for diabetes on employment screening, although he did not seek medical attention for this. He died at age 45 years from a myocardial infarction. Autopsy revealed severe triple-vessel disease, which in the absence of a smoking history, may have been explained by untreated diabetes; the pancreas was presumed to have undergone autolysis. The proband’s deceased paternal grandmother also had an incidentally detected ‘hole in the heart’ and five other relatives had diabetes mellitus.

worldwide: 8.5%). 6 While recognizing the increasing prevalence of type 2 diabetes in urban Indian adults, it is important to note that the prevalence of undiagnosed diabetes in the community is also high. The prevalence of Diabetes and IGT are high in urban Indian population. 7 Risk factors for type 2 diabetes mellitus are: age, gender, ethnicity, family history of type 2 diabetes mellitus, obesity, inactivity, gestational diabetes, macrosomia, hypertension, decreased high-density lipoprotein cholesterol, increased triglycerides, cardiovascular diseases, micro-polycystic ovary syndrome, high blood glucose on previous testing, impaired glucose tolerance and glycated hemoglobin ≥5.7%. 8

Abstract: Diabetes is a chronic disease that occurs when the pancreas is no longer able to make insulin, or when the body cannot make good use of the insulin it produces. Insulin is a hormone made by the pancreas, that acts like a key to let glucose from the food we eat pass from the blood stream into the cells in the body to produce energy. All carbohydrate foods are broken into glucose in the blood. Insulin helps glucose get into the cells. Not being able to produce insulin or use if effectively leads to raised glucose levels in the blood (known as hyperglycemia). Over the long term high glucose levels are associated with the damage to the body and failure of various organs and tissues. Diabetes currently affects more than 62 million Indians, which is more than 7.1% of the adult population. The average on onset is 42.5 years. Nearly 1 million Indians suffer due to diabetes every year. A survey on 75 adults, out of which 32 females and 43 males were done. A well structured questionnaire was employed to interview the subjects about their age, BMI, family history, reproductive health, diet and lifestyle. The data so collected was subjected to statistical analysis using chi square test. The results showed a positive correlation between diabetes risk and age, increased BMI, family history, early marriage, late childbirth, consumption of exogenous hormones). Awareness was also done through a councelling sessions were conducted with the patients to educate the people about early detection of diabetes, the do’s and don'ts, to implement healthy lifestyle interventions, importance of understanding diabetes and poor impact of westernization of lifestyle. Thus, it was concluded from our study that there is a great impact of hormone-related factors and family history in the development of diabetes.

We examined the RRs of ten other autoimmune diseases (Table 3), three of which (rheumatoid arthritis, Sjögren’s syndrome, and systemic lupus erythematosus) showed a significant familial coaggregation associated with type 1 diabetes. A family history of type 1 diabetes was associated with thyroid diseases, specifically, a RR (95% CI) of 1.24 (1.14–1.35) for simple goiter, 1.16 (1.04–1.31) for non- toxic nodular goiter, 1.61 (1.49–1.74) for thyrotoxicosis, 1.78 (1.57–2.01) for acquired hypothyroidism, and 1.66 (1.40–1.98) for thyroiditis. We further tested the coaggrega- tion of type 1 diabetes with epilepsy, as an association was reported in a recent study. However, we found that the RR of epilepsy was not significantly increased (RR, 1.15; 95% CI, 0.97–1.37) in relatives of people with type 1 diabetes compared to the general population (Table 3).

In keeping with previous studies [5–7, 12], class 4–5 variants of GCK, HNF1A, and HNF4A accounted for the large majority (87%) of monogenic diabetes diagnoses. However, the frequency of so-called rare subtypes of monogenic diabetes was unexpectedly high in patients with adult-onset diabetes. In total, HNF1B, ABCC8, KCNJ11, and INS class 4–5 variants accounted for 13% of the cases. In 15 (6%) patients, a class 4–5 HNF1B mo- lecular defect was found. This was unexpected since pa- tients had been excluded from our study when they were known to display classical phenotypes, particularly renal disease, associated with HNF1B [23]. Moreover, this 6% prevalence was higher than an estimation previ- ously reported (< 1%) in patients with a MODY pheno- type but no known renal disease [24]. Renal morphology and renal function were normal in the large majority of our HNF1B-MODY patients (Additional file S2: Table S5). Of note, among the 15 HNF1B cases we identified, 10 had an HNF1B whole gene deletion, known to be as- sociated with a normal renal function at diabetes diagno- sis in 75% of cases, but a severe diabetes phenotype [23]. We also found ABCC8 and KCNJ11 class 4 – 5 gain-of- function variants accounting for 11 (4.4%) cases. Since our study had excluded patients with a personal or a family history of NDM, it confirmed that ABCC8 / KCNJ11 variants can cause a milder form of diabetes that may reveal as adult-onset diabetes [25–28]. It also suggests that variants of the K-ATP channel genes may be involved in monogenic adult-onset diabetes more fre- quently than previously thought.

A multiple linear regression was performed to predict the CKD knowledge score based on age, education, oc- cupation, marital status, family history of kidney failure, and a personal history of hypertension, diabetes, heart disease and stroke. The bivariate analysis showed that participants who refused to reveal their annual income had statistically significantly lower knowledge scores than the other category participants. This participant characteristic was excluded because practically it would not have made a unique contribution to a model that could be used to predict knowledge scores. Table 3 shows the results of the standard multiple regression analysis between CKD knowledge score and participant characteristics. A significant regression equation was found (F (21,921) = 4.58, p < 0.001), with an R 2 of 0.095. The multivariate analysis found higher knowledge scores associated with a higher level of education, such as pos- sessing a postgraduate diploma or bachelor degree and diploma/vocational certificate. A family history of kidney failure was also independently associated with higher knowledge scores, as was a personal history of diabetes. Finally, participants currently or previously within a rela- tionship (married, de-facto, living with a partner or di- vorced/separated/widowed) had significantly higher knowledge scores than those who were single/never married.