Question

You will be using your Framingham dataset to answer the following questions. You will be performing hypothesis testing. For each question, please write out the null hypothesis, alternate hypothesis, which test statistic you will be using (based on variable type). Then report the results from performing the analysis using SPSS. Make sure to report the test statistic, significance level, and whether you will accept or reject the null hypothesis and why. Finally, if you find significant differences, report the proper. descriptive statistics.

1. Are there differences between smokers and nonsmokers in systolic blood pressure?
2. Are there differences between smokers and nonsmokers in total serum cholesterol?
3. Are there differences between smokers and nonsmokers in rates of prevalent coronary heart disease?
4. Are there differences between smokers and nonsmokers in rates of prevalent hypertension?
5. Are there differences for people in different BMI categories (underweight, normal weight, overweight, obese) in systolic blood pressure?
6. Are there differences for people in different BMI categories in total serum cholesterol?
7. Are there differences for people in different BMI categories in rates of prevalent coronary heart disease?
8. Are there differences for people in different BMI categories in rates of prevalent hypertension?

Answer 1

1. Mean daytime systolic and diastolic blood pressures were significantly higher in the smokers, regardless of whether or not they used antihypertensive medication. Mean nocturnal blood pressure readings were similar between smokers and nonsmokers. Mean 24-hour systolic blood pressure readings were significantly higher in the smokers, regardless of whether or not they used antihypertensive medication. Nocturnal dipping was similar for all groups. Blood pressure loads were consistently and significantly higher in the smokers regardless of medication use.
   CONCLUSION: Mean daytime systolic and diastolic blood pressure readings were consistently higher in the smokers when compared to nonsmokers regardless of antihypertension medication use. Nocturnal dipping was similar for smokers and nonsmokers.   

   It is known that smoking causes a temporary rise in blood pressure levels for both hypertensive and normotensive individuals. Nevertheless, epidemiological studies evaluating blood pressure levels using casual in-office blood pressure measurements have shown that smokers present blood pressure readings that are less than or equal to those of nonsmokers. In these studies, individuals are assessed by an isolated blood pressure measurement according to the recommendations of national and international guidelines (JNC 7, ESH 4, IV DBHA). In contrast, smokers submitted to ambulatory blood pressure monitoring (ABPM) present higher mean blood pressure readings than nonsmokers.

   A better understanding of the 24-hour effect of smoking on blood pressure trends, actual systemic blood pressure and the impact on target organs is required. Ambulatory blood pressure monitoring (ABPM) is the diagnostic tool that enables this analysis, providing a profile of daytime and nighttime blood pressure variations. This test provides a better understanding of hypertension for diagnosis, prognosis or treatment purposes. Blood pressure level trends from ABP monitoring as well as measurements taken at home are more reliable in relation to the prognosis of hypertension since they are a better indication of target organ lesions than measurements taken at the doctor's office.

   The objective of this study is to assess the effect of smoking on blood pressure trends during a 24-hour timeframe using the ambulatory blood pressure monitoring parameters.

2. Blood samples for lipid profile were taken in morning with minimum of 8 hours over night fasting. Venous sample were collected in plain vacutainer and transported and processed in biochemistry lab on the same day. The samples were centrifuged (2500×g10 minutes at 4°C) and the plasma thus obtained were used for the estimation of lipid profile. The estimation of lipid profile parameters like the total cholesterol levels, serum triglycerides and HDL were done using ‘CHOD/ PAP method’ in vitro. LDL cholesterol was estimated using the formula Serum LDL = Total cholesterol (mg/dl) -(HDL (mg/dl) + TGL /5(mg/dl)) Non-HDL cholesterol is defined as the difference between total cholesterol and HDL cholesterol and includes all the cholesterol present in lipoprotein particles considered to be atherogenic. It was calculated as total cholesterol minus HDL cholesterol. Risk ratios were calculated as total cholesterol/ HDL cholesterol, LDL cholesterol/HDL cholesterol, non-HDL cholesterol/HDL cholesterol. Statistical analysis The data entry and analysis were done using SPSS version 21.0 (statistical software for social sciences). The statistical significance between two means was tested using independent student t-test and between categorical variables was tested using chi-square test. Pearson correlation test (r value) was used to correlate between two continuous variables. For all statistical tests of significance, a p-value of less than 0.05 was considered significant within 95% confidence limits. RESULTS The mean age of the study participants was 34.7±2.9 years. The mean age between the smokers (mean age= 34.7±2.9 years) and non-smokers (mean age=34.7±3.0 years) was not significantly different as tested by Student t-test. The smokers included in the study were habitual smokers for 5.4±2.9 years on an average. The number of cigarettes smoked per day was over a pack estimating 11.6±4.5 cigarettes per day. The magnitude of overweight(32%) was higher among smokers when compared with the non-smokers (22%).CONCLUSION The serum levels of cholesterol, triglycerides, LDL, VLDL were significantly higher and serum HDL levels were significantly lower among the smokers when compared with the non-smokers. The 10 years cardiovascular risk as measured by the Framingham risk score was significantly higher among smokers when compared to the non-smokers.

3. Smoking has been associated with an increase in morbidity and mortality due to atherosclerotic coronary artery disease, which has been confirmed in several studies . This association results from the multiple noxious effects of smoking on the mechanisms of atherogenesis and thrombosis, and in the vasomotor and arrhythmogenic mechanisms

   Several studies have shown that smoking, at the time of acute myocardial infarction, is not a risk factor for subsequent death . Smokers have a better in-hospital clinical evolution independent of thrombolytic therapy , and some studies  have reported that smokers tend to have a better prognosis. Surprisingly, smoking has even appeared as a factor of better prognosis after the event

   Some multicenter studies, however, have emphasized that after adjusting for age and other variables, no statistical difference exists in the in-hospital and out-of-hospital prognosis between smokers and nonsmokers

   This study aimed at assessing the impact of smoking on in-hospital morbidity and mortality of patients experiencing acute myocardial infarction and to assess the association of smoking with other cardiovascular risk factors and clinical data.

   Methods

   A prospective cohort study was carried out with 121 patients diagnosed with acute myocardial infarction, who were referred to the Central Emergency Unit of the Santa Casa de Misericórdia of São Paulo from January 1999 to June 2000. Their diagnosis was based on clinical and electrocardiographic criteria and on the elevation of cardiac enzymes (MB fraction of creatine phosphokinase) 9, which were followed up during the entire length of hospitalization.

   The patients were divided into 3 groups as follows: smokers, ex-smokers, and nonsmokers. Smokers were the patients who smoked regularly for at least 1 year before the myocardial infarction; ex-smokers were those who smoked regularly but interrupted their habit at least 1 year before the infarction; nonsmokers never smoked.

   Each patient was interviewed so that the risk profile of cardiovascular diseases could be determined. The following data were assessed: age, sex, and personal antecedents, such as systemic arterial hypertension, diabetes mellitus, alcoholism, dyslipidemia, angina pectoris, hyperuricemia, obesity, sedentary life style, and previous myocardial infarction.

   On the patients' admission, their clinical conditions were assessed using the variables time to reach the service and Killip classification on admission. The patients were reclassified after 24 hours (Killip).

   Assessment of morbidity and mortality in each group was based on data for each patient: in-hospital complications – arrhythmias, acute pulmonary edema, cardiogenic shock, stroke, reinfarction, pulmonary thromboembolism, deep venous thrombosis, and death.

   The chi-square test was used for assessing the association of smoking with other risk factors, time to reach the hospital, Killip on hospital admission, and use of a thrombolytic agent. Analyzing the risk factors for morbidity and mortality (death, arrhythmias, shock, and others), the odds ratio for each variable studied and the respective confidence intervals were calculated. The statistical significance level adopted was 5% for all tests.

   Results

   The study assessed 121 patients, 54 (44.6%) smokers, 32 (26.4%) ex-smokers and 35 (29%) nonsmokers. Three patients were excluded from the study because they died immediately after entering the hospital.

   A statistically significant association was observed between smoking and sex (P=0.001), alcoholism (P=0.03), diabetes (P=0.02), and age (P=0.01). No statistically significant associations were found between smoking and the other risk factors studied

   significant increase in the risk of acute pulmonary edema was observed in the ex-smokers as compared with that in the nonsmokers (OR=9.5; 95% CI).

   The smokers showed a reduction in the risk of death, shock, infection. This reduction, however, was not statistically significant for any event. On the other hand, an increase in the risk of ventricular fibrillation/ventricular tachycardia, hypotension, and acute pulmonary edema was observed, but also with no statistical significance

   A reduction in the risk of death, shock, and infection was also observed, but with no statistical significance for the ex-smokers. In this group, an increase in the risk of ventricular fibrillation/ventricular tachycardia and congestive heart failure was observed, but with no statistical significance.

   Discussion

   Several hypotheses have already been formulated to explain the paradox of better prognosis in the group of smokers. Smokers experience acute myocardial infarction at a younger age and, therefore, have a more benign risk profile. In addition, acute myocardial infarction is usually seen as the result of 2 pathogenic mechanisms (atherosclerosis and thrombosis), whose balance is altered by smoking. Due to the thrombogenic effect of cigarette smoking, smokers experiencing infarction may have a relatively smaller atherosclerotic lesion and more thrombi in the site of coronary occlusion, and, consequently, they may respond better to thrombolytic therapy . Another apparent cause of this paradox is that a great proportion of smokers die prior to hospital admission. Yet, evidence exists that the sites of infarction differ between smokers and nonsmokers

   In this study, smokers were younger than nonsmokers, which is in accordance with the findings of several published studies . The GISSI 2 Study (Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico) reported that smokers were approximately 11 years younger than nonsmokers , and the Israeli Thrombolytic National Survey (ITNS) reported that smokers were on average 10 years younger than nonsmokers . These 2 studies also reported a greater frequency of female patients among nonsmokers, which was also observed in our study. In addition, smoking was not significantly associated with the variables death, shock and infection.

   Another factor emphasized in the literature is that patient's admission to the hospital within 6 hours or less from symptom onset was more common among smokers. One may assume that smokers are more prepared or aware of the symptoms of acute coronary heart disease. In our study, the proportion of patients entering the emergency unit within 6 hours after symptom onset was greater in the group of smokers, but with no statistical significance.

   No statistically significant association was observed between smoking and the various risk factors for acute myocardial infarction, such as hypertension, obesity, angina, sedentary lifestyle, dyslipidemia, and uric acid. Only diabetes and alcoholism were associated with smoking. Although not significantly, smokers usually have a more benign risk profile than do nonsmokers and ex-smokers, and this has also been reported in other studies .

   In the literature, a greater incidence of ventricular fibrillation and tachycardia has been reported in smokers, while a greater incidence of death and other complications (cardiogenic shock, infections, severe mitral regurgitation, atrial fibrillation) has been reported in nonsmokers . Smoking increased the risk of ventricular fibrillation/ventricular tachycardia and hypotension, but with no statistical significance. A greater risk of cardiogenic shock, infections, and death was observed in nonsmokers, but with no statistical significance . The ITNS and the GISSI-2 studies assessed, respectively, 999 and 9720 patients.

   Like smokers, ex-smokers were younger and predominantly males. In regard to the other risk factors studied, the groups had similar percentages. Therefore, we could assume that ex-smokers would have a similar probability of experiencing a cardiovascular event if they had not smoked. Smoking might have accelerated the atherosclerotic disease, causing ex-smokers to experience acute myocardial infarction at a younger age than nonsmokers.

   In regard to the time elapsed between symptom onset and arrival at the hospital, ex-smokers had percentages closer to those of smokers, which could be a cause for their better prognosis as compared with that of nonsmokers.

   Acute pulmonary edema was the most surprising variable, with, in disagreement with the literature, the greatest statistically significant risk in ex-smokers. Coincidentally, the patients studied may have had some type of previous ventricular dysfunction, which could help to explain the lack of statistical significance in the complications between both groups.

   In conclusion, in our study, an association between smoking and some cardiovascular risk factors was observed. However, no statistical difference in morbidity and mortality was observed between the groups studied, except in the variable acute pulmonary edema.

4. We found no difference in the prevalence of the diagnosis of hypertension according to smoking status in the Czech Republic (non-smokers, smokers and former smokers) after adjusting for body mass index and age, although univariate analysis found former smokers less likely to be hypertensive compared to non-smokers. Possible explanation of the univariate analysis result is weight-correlated presence of hypertension - former smokers weighed more compared to non-smokers. There was no difference in the prevalence of the diagnosis of hypertension among smokers after adjusting for age and body mass index.

   Our findings agree with those of Halimi et al., Onat et al. and Jazon et al. , which also found a difference in the prevalence of hypertension according to smoking status which is connected to BMI. Adjusting for age and BMI is important, because age is involved in the pathogenesis of hypertension and the majority of former smokers gain weight after smoking cessation . Post-cessation weight gain is multifactorial. Probably one of the most important causes is nicotine itself, because it increases the basal metabolic rate by up to 10 % via sympathetic stimulated thermogenesis and oxidation of fatty acids . Additionally, Mineur et al. found nicotine mediated stimulation of proopiomelanocortin system (POMC) resulting in decrease of appetite in smokers .

   In contrast, Lee et al. reported higher levels of both systolic and diastolic BP among those who had quit smoking for ≥ 1 year in a 4-year prospective study . The authors monitored 8,170 male steel workers who were examined in 1994 and re-examined in 1998 . Higher BP values in former smokers were similar to weight gainers as well as weight losers and maintainers. All data were adjusted for baseline BMI, age, alcohol consumption (grams per week), cigarette smoking (pack-years), exercise (times per week), family history of hypertension, systolic BP or diastolic BP (baseline for the dependent variable), as well as changes in BMI and alcohol consumption during the follow-up period. Stratified analyses based on weight changes during 4 years were included. One possible explanation is the ACTH increase during smoking abstinence . Smoking damages the vessel wall, possibly increasing the synthesis of prostacyclin and enhances the interaction between platelets and vessel wall . These changes lead to decreased aorta elasticity . Increased arterial stiffness can persist up to 10 years after smoking cessation , and may possibly increase the prevalence of hypertension among former smokers.

   We found that those suffering from hypertension tend to be older, as well as overweight or obese. A higher prevalence of hypertension in older adults is generally known, as well as the higher prevalence of hypertension among those being overweight or obese. It has been described, that overweight/obese patients suffer more often by chronic medical disorders including arterial hypertension.

   In our sample, more smokers were male gender and had lower education level. This finding reflects current knowledge and present situation in the Czech Republic, with higher prevalence among those with lower socioeconomic status and among male gender

   Our data showed that former smokers were older compared to non-smokers and smokers. Older age among former smokers was noted also by other authors and may be explained by decreasing smoking prevalence with rising age as a consequence of health problems and/or due to concerns about health. Age can also be perceived as a cumulative measure with a greater probability of older individuals compared to younger ones being former smokers. Another explanation is survival bias due to greater survival of former smokers when compared to individuals who continue to smoke .

   We also found that non-smokers weighed less compared to former and current smokers. Higher BMI among former smokers compared to smokers and non-smokers corresponds with results of large population studies and is caused by post-cessation weight gain. In addition, large population studies also described lower BMI of smokers compared to non-smokers [18, 37–41]. As mentioned above, nicotine acts as anorectic by increasing the basal metabolism and decreasing appetite.

   The limitation of the study is its cross sectional design. This type of study allows to estimate the percentage of sick persons as well as persons with risk factor in the population, but there is no possibility to determine whether exposure preceeded the disease or vice versa. Furthemore, cross-sectional design has the impact on the outcomes of the study due to lack of associations. Finally, it is important to acknowledge that some confounding factors may limit the study results. One can argue, that most differences between the groups of former and current smokers could be explained by the different age distribution, as former smokers are older in average . Also, obese people with a diagnosis of hypertension may be more likely to quit, based upon the doctor’s recommendation. Moreover, smokers could be less likely to be diagnosed with hypertension, as they usually don’t visit their doctor or participate in routine blood pressure measurements as often as non-smokers, but such data for the Czech population are not available.

   Another limitation was the self-report of diagnosis of hypertension, especially if we consider that not all patients suffering from hypertension are diagnosed, properly treated or reported the diagnosis. In the representative sample of Czech population, approximately 40 % of adult population aged 25–64 years suffered from hypertension, while almost 30 % of patients with hypertension did not know about this diagnosis . Additionally, high proportion of patients knowing about the diagnosis of hypertension, are not treated adequately . Finally, smoking status was self-reported as well, thus some smokers may pretend to be former smokers or non-smokers and vice versa.

   In this cross-sectional survey, the response rate was not recorded. The sample was selected by a quota choice and is representative in the proportional representation of participants according to age, gender, education, region of residence and the size of settlement. It can be stated, that the sample representativeness is of a limited value. But, when compared to the Czech adult population, our sample seems to be highly representative.

   Additionally, it is important to acknowledge, that these data reflect the situation in the Czech Republic and may not be applicable to other countries.

   As the post-cessation weight gain may increase the risk of developing hypertension, the practical implication of this study is to prevent weight gain and proceed with BP measurements more frequently during and after smoking cessation.

   Conclusion

   In conclusion, no difference in the prevalence of the diagnosis of hypertension according to smoking status after adjusting for age and body mass index was found in our work.   

5. The aim is to study the blood pressure and body dimensions and to find out the prevalence of overweight/obesity and hypertension among adults.

   Materials and Methods:

   A cross-sectional study was conducted of all the people belonging to the Punjabi community, residing in Roshanara area and Jaina building in Delhi, for the past 20 years and aged 18-50 years. The men were engaged in transport business and women were mainly housewives.

   Results:

   Mean values of all the measurements, that is, height, weight, upper arm circumference, pulse rate, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were higher among males as compared with females, except skinfold thicknesses. Body mass index (BMI) and fat percentage was found to be higher among females as compared with males. There was a significant positive correlation between BMI, fat percentage, and blood pressure both SBP as well as DBP. Odds ratio showed that overweight/obese subjects were more likely to have hypertension than those with normal BMI.

   Conclusion:

   Prevalence of prehypertension among overweight/obese suggested an early clinical detection of prehypertension and intervention including life style modification, particularly weight management.    the basic measurements of males and females and the difference between the two genders for the same. Mean values of height, weight, upper arm circumference, calf circumference, pulse rate, SBP, and DBP were found to be significantly higher in males as compared with females. The mean values of all the skinfold thicknesses, that is, biceps, triceps, subscapular, and suprailiac were higher among females than males. Similarly mean values of BMI and fat percentage were also higher among females. Age was found to have positive and statistically significant correlation with both SBP (r = 0.21, P < 0.01) and DBP (r = 0.18, P < 0.01) among males and for females the correlation between age and blood pressure (SBP and DBP) was r = 0.44, P < 0.01 and r = 0.27, P < 0.01, respectively. There was statistically significant positive correlation between blood pressure (both SBP and DBP) and anthropometric measurements, pulse rate, fat percentage

6. A total of 305 patients (men, 132; women, 173) with T2DM visiting an Outpatient department in Northwest General Hospital and Research Centre, Peshawar from January 2016 to July 2016 were included in this study. The whole blood and sera were analyzed for Glycated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TGs), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C). The correlation of BMI with lipid ratios and individual lipid indices were analysed.

   Results:

   Mean of BMI was 29.29±5.23. Dyslipidemia; increased TC, increased LDL-C, increased triglyceride and decreased HDL-C were noted in 40.7%, 54.1%, 69.5% and 41% respectively. The mean difference of LDL-C (p=0.006) was significant between male and female. BMI, TC, TGs, and LDL-C showed no significant correlation where as a significant negative correlation between BMI and HDL-C was observed (r=-0.125, p=0.029, R=0.016). The mean values of TC, TG, LDL-C, TC/ HDL-C and LDL/HDL were greater in patients with normal BMI compared to overweight and obese; however, the differences were not significant. HDL-C differed significantly in BMI groups (p=0.040).

   Conclusion:

   A significant negative correlation between BMI and HDL-C was observed, while the correlation between BMI and LDL-C was observed to be insignificant. HDL-C was found significantly higher in patients with normal BMI. These results are important to indicate that there is modest impact of BMI on lipid profile. Therefore, assessment and management for altered blood lipids should not be based on a patient’s body weight or BMI.

7. Overweight and obesity are defined by the World Health Organization as abnormal or excessive fat that accumulate and present a risk to health. Obesity is measured in body mass index (BMI), which is a person’s weight (in kilograms) divided by the square of his or her height (in meters). A person with a BMI of 30 or more is generally considered obese. A person with a BMI equal to or more than 25 is considered overweight.

   The degree and incidence of obesity in the U.S. has been increasing in both adults and children.In the United States, nearly 70% of adults are classified as overweight or obese.An estimate of 2.6 million deaths worldwide and 2.3% of the global burden of disease are caused by obesity. Obesity was found to be a major risk factor for the development of type-2 diabetes, asthma, hypertension, stroke, coronary artery disease, cancer and cancer-related mortality, liver and gallbladder diseases, sleep apnea, osteoarthritis, and gynecological complications. Obesity also adds to risk once the levels of coexisting risk factors are taken into account. Obesity is associated with elevated blood pressure, blood lipids, and blood glucose; changes in body weight are coincident with changes in these risk factors for disease.

   Cardiovascular disease (CVD) mortality and morbidity has been shown to be elevated in individuals who are overweight, particularly with central deposition of adipose tissues. Abdominal obesity has been shown to be a risk factor for CVD worldwide.Obesity may be associated with hypertension, dyslipidemia, diabetes, or insulin resistance, and elevated levels of fibrinogen and C-reactive protein, all of which increase the risk of CVD events.

   In addition to CVD, obesity has been shown to increase the risk of high blood pressure (HBP). Persistent hypertension is one of the risk factors for stroke, myocardial infarction (MI), heart failure, and arterial aneurysm, and is a leading cause of chronic kidney failure. Moderate elevation of arterial blood pressure leads to shortened life expectancy, which also increases the risk of heart diseases   Body mass index (weight in kg divided by height) data were obtained from the Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance System (BRFSS) [see: http://apps.nccd.cdc.gov/BRFSS]. Data of individuals with BMI >30 (grouped by gender and race) were collected for the U.S., Mississippi, Alabama, Louisiana, Tennessee, and Colorado from 2005–2009. The data for cardiovascular diseases were also obtained from the BRFSS. The proportion of respondents with MI was the percentage of people who answered yes when asked if they had ever been told they “had a heart attack (myocardial infarction)”; and the proportion with stroke was the percentage who answered yes when asked if they had ever been told they had “a stroke.” High blood pressure rates were determined using the proportion of respondents who had been told that they had high blood pressure. All variables were grouped by gender and race.   Results of this study are summarized in Table 1. Analysis of variance showed a significant increase in obesity rates over the past five years in all states and in the U.S. (p<.001). During the examined years, Mississippi had the highest rate of obesity (31.75 ± 1.20%); followed by Louisiana (30.75 ± 0.07%), Alabama (29.9 ± 1.41%), and Tennessee (29.05 ± 2.33%). All four Southern states differed significantly from Colorado (p<.001), which had the lowest rate of obesity (18.55 ± 1.06). The national obesity rate was highest among African Americans (35.90 ± 1.27%) of all racial/ethnic groups.   In conclusion, obesity is a chronic metabolic disorder associated with CVD and the increased mortality and morbidity. Our results have indicated a low association between obesity and MI rates and a moderate association with stroke rates when BMI was used as obesity indicator. However, a strong association was found with HBP when BMI rates were used as obesity indicator. Obesity and HBP modeling with regression and neural network may provide a reliable understanding of the problem, which may aid in the design of new and better interventions to slow and reverse the epidemic.

8. We obtained data on T2DM in Kuwaiti natives from Kuwait Health Network Registry. We considered 1339 comorbid individuals with onset of hypertension preceding that of T2DM, and 3496 non-hypertensive individuals but with T2DM. Multiple linear regressions, ANOVA tests, and Cox proportional hazards models were used to quantify the impact of hypertension on correlation of BMI with age at onset and risk of T2DM.

   Results

   Impact of increasing levels of BMI on age at onset ot T2DM is seen augmented in patients diagnosed with hypertension. We find that the slope of the inverse linear relationship between BMI and onset age of T2DM is much steep in hypertensive patients (−0.69, males and −0.39, females) than in non-hypertensive patients (−0.36, males and −0.17, females). The decline in onset age for an unit increase of BMI is two-fold in males than in females. Upon considering BMI as a categorical variable, we find that while the mean onset age of T2DM in hypertensive patients decreases by as much as 5–12 years in every higher BMI categories, significant decrease in non-hypertensive patients exists only when severely obese. Hazard due to hypertension (against the baseline of non-hypertension and normal weight) increases at least two-fold in every obese category. While males have higher hazard due to hypertension in early adulthood, females have higher hazard in late adulthood.

   Conclusion

   Pre-existing condition of hypertension augments the association of BMI with Type 2 diabetes onset in both males and females. The presented results provide health professionals directives on the extent of weight-loss required to delay onset of Type 2 diabetes in hypertensive versus non-hypertensive patients.