Early Obesity Counselling in Primary Care Setting Could Decrease Metabolic Diseases Are Diabetes and Hypertension Avoidable, if Stopping Early Weight Gain?

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Imre RURIK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4426620/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Obesity management is a big challenge for health care providers. Primary care is the appropriate level, not only for the management but for the prevention and early recognition as well. Obesity develops gradually and needs attention in the early phase of weight gain. The main results of four Hungarian and international studies in primary care settings were summarized, seeking relationship between weight gain in younger life and development of metabolic diseases. Data of primary care patients were collected about the changes of their weight gain from 20y to the present. Source: medical files and self-reports. Early weight-gain between 20y and 30y means a serious risk for developing diabetes, between 30y and 40y for hypertension and even faster weight-gain could be a risk factor for both metabolic diseases. In females, significant weight gain around pregnancies and the menopause could increase the risk of these morbidities as well. Primary care service providers/family physicians/general practitioners ought to be not only an inactive observers, they have to give more focus on those of their patients who show conspicuous weigh gain in their younger decades, to explore the individual reasons and to initiate the appropriate intervention as early as possible. diabetes family medicine hypertension metabolic diseases obesity prevention primary care Figures Figure 1 INTRODUCTION The prevalence of obesity is increasing rapidly in all age groups worldwide. It is currently one of the fastest growing epidemics, affecting 10–40% of the adult population. Reduced life expectancy and obesity related complications may account for 5–10% of all health costs in the European countries [ 1 ].Obesity increases the risk of adverse health conditions, the risk of metabolic and cardiovascular diseases, certain type of cancers, impaired quality of life, and (as recently discovered) complications from Covid-19 [ 2 ]. During the normal ageing process, weight gain often occurs gradually over decades (about 0.4–0.5 kg per year), making it difficult for most people to perceive the specific causes [ 3 ]. Prevention of obesity is not always easy; there are some essential contributing factors, i.e. genetic background, lifestyle, medication, environmental circumstances etc. Since weight stability requires balance between energy consumed and expended, in most of the cases, the advice to “eat less and exercise more” seems to be straightforward in the prevention [ 4 ]. It should be started as early as possible; therefore interventions preferably in childhood could be implemented for prevention. Obesity management requires holistic approach, analysis of individual risk factors and it is a time consuming, not always successful process. Excess body weight during childhood is associated with an increased risk of adult type 2 diabetes only if it continued until puberty or later ages [ 5 ]. In addition to the robust evidence that obesity and excess body weight are strongly related to the incidence of metabolic diseases (type 2 diabetes, hypertension), the rate of weight gain could also contribute to the occurrence of these morbidities. The rate of weight gain was evaluated in studies among middle-aged and elderly populations over a few years of follow up periods [ 6 , 7 ]. None of the primary care studies focused to this phenomenon and it was the reason why our research started, initiated in the primary care setting, the first line of the health care system. Our first Hungarian lifelong study, in which the two main components of Metabolic Syndrome were evaluated [ 8 ] and the following others found differences between the dynamics of weight gain among patients with obesity, diabetes and hypertension [ 9 , 10 ]. A medical setting is required to control laboratory parameters (plasma glucose level, lipid profile), blood pressure is often measured by the patients, while body weight and height are usually checked by the individuals themselves. Although many people regularly measure their own body weight, remember these data, they rarely store or compare these records. The aims of this paper are to present and summarize our previous findings; how the weight gain trends to overweight and obesity in the younger decades and could be responsible for the onset of metabolic diseases and to explain why early primary care intervention is needed. METHODS and PATIENTS Study design A cross-sectional survey, combined with retrospective analysis. Selection of participants Patients (aged between 60 and 70 years) who visited the offices of their family physicians/general practitioners or primary care outpatient clinics within the study period for any reason (having actual complaints, coming for regular screening, drug prescription, etc.) were consecutively selected. Data collection Having been informed about the aim of the survey and given the proper instructions, the selected participants were asked to fill in a short one page questionnaire in the doctor’s office. The form contained questions regarding self-recorded data of body weight in each decade of life (at 20, 30, 40, 50, and 60 years of age), the most recent and the highest values; body height at 20 years and recently. For women, the weight before the first pregnancy, after the last delivery, before and after menopause was also requested. Primarily, if were available or recorded previously, data in the medical files were used, otherwise the self-reported data were considered. The recent corresponding measures were performed by the staff and they added the time of diagnoses as needed [ 8 , 9 , 10 ]. In the international study, the questionnaire in English was translated to their respective national language by the family physicians, who participated in the national arms of the study in Germany, Hungary, Italy, Slovakia and Ukraine [ 11 ]. (See Appendix ). The protocol of the first Hungarian study [ 8 ] was continuously improved using more developed methods in order to drop out unreliable data. Questions regarding pregnancy and menopause were added in the last (international) study [ 11 ]. Settings The participating practices were located in the suburb of Budapest , in Debrecen and Kecskemét (Hungary), Leipzig (East-Germany), in Rome , around Naples (South-Italy), in Bratislava (Slovakia) and near to Uzhgorod (West-Ukraine). Practice sizes varied between 1200–1800 enrolled patients, 56–112 subjects participated from each practice. The duration of the study was usually one week. Exclusion criteria The primary exclusion criteria were: serious visual, acoustic or mental impairment, participation in our previous studies or simple refusal to participate. In order to reduce methodological bias, the validity of self-reported data was checked. After completing the questionnaire, the participants’ current body weight and height were measured and subjects (approx.20%) were excluded if the difference between the self-reported and the recently recorded data exceeded 3 kg or 5cm. Quantitative variables The participants were divided into four groups: patients with diabetes ( DM ), with hypertension ( Hyp ), or both ( HyDm ) and a control group (people having neither diabetes nor hypertension). For another comparison, patients with both illnesses were counted among diabetics. After weight and height measurements the body mass index (BMI) was calculated. Percentage of weight change was calculated as weight at examined decade/ weight at previous decade. The overall incidence rate was calculated as the ratio between the numbers of cases. Hypertension and diabetes were used as an outcome measure. Independent variables included gender, age and BMI categories. Statistics In the different studies, the following statistical methodswere used: Student t-test, logistic regression (OR and CI), ANOVA, Fisher and κ 2 test, Cox-regression model, Schönefeld residual test. The analyses were performed using SPSS and later on STATA 10.1. softwares. RESULTS Altogether 2 468 persons, above 60 years participated in the four studies. Their distributions regarding referenced studies: Reference No All Men Women 8 354 159 195 9 540 225 315 10 815 319 496 11 759 337 422 Within the results of the studies, there was no statistical difference between the mean ages of the established diagnoses. Diagnosing hypertension was 55.4 (± 15.9) (median: 59) years in men and 53.8 (± 15.7) (median: 56) in women, while diabetes was diagnosed at the age of 51.3 (± 17.1) (median:53) years and 54.4 (± 13.1) (median: 50) years in males and females, respectively [ 10 ]. Higher age at the recognition of diabetes 58.2 ± 8.7 years was recorded only in one study and only by men [ 11 ]. Five hundred forty patients were involved in our second study [ 9 ]. The most important phenomena, the faster weight gain in the early decades by men with diabetes and hypertension were demonstrated on Tables 1 . and by women on Table 2 . Table 1 Means of self-recorded current and maximum weight [kg] and weight gain between decades [Δ], and last decade prior to diagnosis of men , with hypertension ( Hyp ), with diabetes ( DM ), with both ( HyDm ) and with neither disease (control group- None ) * Significant (p < 0.05) difference between decades of different diagnostic groups [ 9 ]. MEN None Hyp DM HyDm n : 225 61 104 38 22 Age decades Δ Δ Δ Δ 20 years 67.6 66.8 73.0 69.7 30 years 68.9 1.3 69.8 1.0 79.1 *6.1 71.2 1.5 40 years 70.2 1.3 73.1 3.3 82.1 3.0 72.1 1.1 50 years 72.9 2.7 76.2 3.1 84.2 2.1 75.2 3.1 60 years 75.1 2.2 76.6 0.4 89.5 *5.3 79.8 *4.6 Current 75.7 0.6 77.4 0.8 90.2 1.3 84.1 5.3 Last prior to diagnosis 1.8 3.2 3.3 maximum 82.6 82.1 91.1 90.9 Table 2 Means of self-recorded current and maximum weight [kg] and weight gain between decades [Δ], and last decade prior to diagnosis of women , with hypertension ( Hyp ), (with diabetes ( DM ), with both ( HyDm ), and with neither disease (control group- None ) WOMEN None Hyp DM HyDm n: 315 65 180 42 28 Age decades Δ Δ Δ Δ 20 years 54.8 54.1 59.1 60.2 30 years 58.1 3.3 56.2 2.1 60.2 1.1 63.2 3.0 40 years 61.0 2.9 59.8 3.6 71.1 *10.9 67.9 *4.7 50 years 65.9 4.9 64.0 4.2 73.6 2.5 69.0 1.1 60 years 66.5 0.6 67.9 *3.9 77.0 *3.4 69.2 0.2 Current 67.9 1.4 68.6 0.7 78.5 1.5 73.9 4.7 Last prior to diagnosis 3.9 6.3 6.4 Maximum 69.7 73.3 81.9 82.6 * Significant (p < 0.05) difference between decades of different diagnostic groups [ 9 ]. In the fourth study [ 11 ] a lifelong body weight gains were observed in all diagnostic categories and genders (Table 3 . ). The last measured data were usually smaller then the “peaks” within previous decades.Diabetes and hypertension were diagnosed earlier among patients with both morbidities, while hypertension alone occurred later. Diabetic men reported their highest weight gain between 20-30y of age, when expressed both in [kg] and in percent of the weight at 20y . It was usually followed another fast weight gain in the last decade before diabetes was diagnosed. The highest early increase was recorded among men with both morbidities. Patients with hypertension had higher, but not always significant differences through decades than the control group had. These changes were more markedly reflected in the figures of BMI (Table 4 . ) . Similar data of women are presented on the Table 5 . and 6 . Table 3. Men . Data of recorded body weight (means and 95% confidence intervals), their changes ( Δ) by decades (in kg and in % of the weight at previous decade). Mean ages at the time of diagnoses. Abbreviation used in the table: DM -diabetics, Hyp -hypertensive, HyDm -patients with both hypertension and diabetes, None (control-patients) free from these pathologies; s:significant, ns:non-significant. [11]. Table 4 . Men. Data of calculated BMI s (means and 95% confidence intervals), their changes ( Δ) by decades (in kg/m 2 and in % of the BMI at previous decade) Abbreviation used in the table: DM -diabetics, Hyp -hypertensive, HyDm -patients with both hypertension and diabetes, None (control-patients) free from these pathologies; s:significant, ns:non-significant. [11]. Table 5. Women . Data of recorded body weights (means and 95% confidence intervals), their changes ( Δ) by decades (in kg and in % of the weight previous decade) Mean ages at the time of diagnoses. Abbreviation used in the table: DM -diabetics, Hyp -hypertensive, HyDm -patients with both hypertension and diabetes None (control-patients) free from these pathologies; s:significant, ns:non-significant. [11]. Table 6. Women . Data of calculated BMIs (means and 95% confidence intervals), their changes ( Δ) by decades (in kg/m 2 and in % of the BMI at previous decade) Abbreviation used in the table: DM -diabetics, Hyp -hypertensive, HyDm -patients with both hypertension and diabetes, None (control-patients) free from these pathologies; s:significant, ns:non-significant. [11]. The tables demonstrate that both men and women who became diabetics later on had higher body weight and BMI at the age of 20. For diabetes, the highest increases were reported in the third, for hypertension in the fourth life decades and the increase also continued in the fifth decade. For most of the affected patients, high increase was recorded in the last decade prior to diagnosis. No differences were found between countries. After 60y, a small decrease was observed by both genders in all of the studies. The differences between weight gains of diagnostic groups were more visible, when diabetics and patients with diabetes and hypertension were merged into one group and were compared with the control group (Fig. 1 .). In the third study [ 10 ], patients were divided into quintiles (N = 127–141) according to the distribution of data of weight changes between the respective and baseline decades, as seen in Table 7 . Data of other quintiles (q) were compared to that of q1 and between genders. There were lower differences between data in the first decade therefore only three groups were formed here. Incidence rate (IR) and hazard ratio (HR) of both illnesses were the highest in the group q5 where the largest increases were registered. HR was even higher when both hypertension and diabetes were diagnosed. Higher increase in body weight also resulted in the changes between BMI categories. Patients who belonged to the normal BMI category, had 2.80 odds ratio (OR) (95% CI: 1.24–6.25; p = 0.013) for diabetes when changing to overweight and 3.23 OR (95% CI: 1.19–8.76; p = 0.022) when moving into the obese category. Table 7. [10]. Decades long weight changes by gender and co-morbidity (in quintiles). IR and HR for hypertension and diabetes . Dec- life decades of patients y -year. Range - upper and lower values of quintiles with equally distributed data (N=127-141 at age 20y), -calculated from weight at respective decade/ weight at 20y. IR - incidence rate of the respective morbidity (expressed in cases per 100 persons-years) (relevant IR for Male* is the same for all decades). HR - hazard ratio of developing morbidity (diabetes or hypertension) adjusted for gender, age, and co-morbidity. CI- confidence interval. p - level of significance Co-morb ( Co-morbidity ) - No : diabetes or hypertension only, Yes : both are present (relevant IR without *co-morbidity is the same for all decades) Comparing the date of weight change between the initial and the respective decades, the higher the weight change was, the higher HR for diabetes and hypertension were calculated. Table 8 . presents these ratios only for 10% and 30% increases in weight between decades. Early increase of body weight was related to higher HR for men for developing diabetes and for women for developing hypertension. In many cases, the increases were much higher than 30%, often exceeding 50%. Table 8 [ 10 ]. Weight changes between decades and changes of hazard ratios n = 815 changes by decades Hypertension Diabetes 20-30y Adjusted HR (95% CI) p Adjusted HR (95% CI) p 10% 1.31 (1.08–1.58) 0.006 1.24 (0.92–1.66) 0.162 30% 2.23 (1.26–3.97) 1.89 (0.78–4.59) Male* 1.00 1.00 Female 1.28 (1.03–1.59) 0.024 0.69 (0.49–0.98 0.036 Co-mor No* 1.00 1.00 Yes 1.51 (1.20–1.89) 0.001 1.75 (1.10–2.80) 0.019 30-40y 10% 1.16 (1.03–1.30) 0.011 1.35 (1.13–1.63) 0.001 30% 1.55 (1.10–2.18) 2.48 (1.43–4.30) Female 1.32 (1.06–1.64) 0.013 0.67 (0.47–0.97) 0.033 Co-morb 1.40 (1.11–1.77) 0.004 1.57 (0.98–2.52) 0.061 40-50y 10% 1.09 (0.98–1.22) 0.125 1.25 (1.09–1.43) 0.002 30% 1.31 (0.93–1.84) 1.93 (1.28–2.92) Female 1.34 (1.07–1.67) 0.010 0.70 (0.49–1.01) 0.056 Co-morb 1.42 (1.12–1.79) 0.003 1.66 (1.02–2.68) 0.039 50-60y 10% 1.02 (0.91–1.14) 0.791 1.08 (0.90–1.30) 0.398 30% 1.05 (0.75–1.47) 1.27 (0.73–2.12) Female 1.34 (1.07–1.68) 0.010 0.79 (0.55–1.14) 0.203 Co-morb 1.47 (1.16–1.86) 0.002 1.82 (1.11–2.97) 0.017 60-70y 10% 0.85 (0.62–1.17) 0.317 0.92 (0.48–1.77) 0.811 30% 0.62 (0.24–1.59) 0.79 (0.11–5.50) Female 1.08 (0.55–2.14) 0.811 0.48 (0.13–1.63) 0.240 Co-morb 2.03 (1.00-4.12) 0.051 Dec- life decades of patients . y -year. HR - hazard ratio of developing morbidity (diabetes or hypertension) adjusted for gender, age, and co-morbidity. p - level of significance Co-morbidity - No : diabetes or hypertension only (*relevant for all decades) Yes both are present *HR of male is relevant for all decades Among women, weight changes around delivery and menopause had also an impact on the development of diabetes and hypertension. The means of body weight, registered by the participants at the last delivery and at the age when menopause related symptoms occurred, correlated significantly with diabetes and/or hypertension resulting higher probabilities for developing the diseases (Table 9 ). There were no significant differences between numbers of children and the weight gain of the mother. Table 9 Women . Mean body weight at the age of 20y, after last delivery, in the last decade before and around menopause. Odds of diabetes and diabetes + hypertension (OR, 95% CI and p-value) [ 11 ]. WOMEN n = 408 Weight Mean [kg] Diabetes Diabetes + Hypertension OR 95% CI p-value OR 95% CI p-value 20 years 56.53 0.98 [0.95–1.02] 0.62 0.99 [0.96–1.03] 0.81 last delivery 69.15 1.04 [1.01–1.07] 0.003 1.04 [1.01–1.07] 0.019 last decade before menopause 61.82 0.95 [0.91–1.01] 0.14 0.95 [0.91–1.01] 0.16 at menopause 67.64 1.07 [1.01–1.13] 0.01 1.07 [1.01–1.13] 0.008 DISCUSSION Main findings Early weight gain resulting overweight and obesity is significantly associated to the incidence of metabolic diseases. Patients with diabetes recorded the highest increase expressed in body weight, in BMI and in percent of the weight at 20y. Body weight and BMI changes over decades, was the highest in men with diabetes, higher among patients with hypertension and the less within control groups in both gender. Among patients with diabetes and hypertension, both diagnoses were set up earlier, than by patients having only one of them. Although minimal differences were proven between surveys, the trend was clear, confirming the harmful effects of early weight gain in the younger decades, with small differences between genders. Significant weight gain around the (last) delivery and menopause increase the chance for diabetes and/or hypertension as well. Strengths and limitations Decade-long comparison of data could be mentioned as the real strength of this study. The most important methodological limitation was the reliability of data based on memory and personal records, which could err in both directions. The medical records used could not counterbalance this bias with certainty. Unfortunately, regular body weight measurement is not part of the routine medical practice. People usually remember better at what age and what life event they started to gain more weight (e.g. marriage, divorcing, graduation, changing work places or accommodation, pregnancies, menopause, required changes of “wardrobe”, quitting smoking, getting ill or hospitalized, etc.).Although there could be some unreliability of the reported data, the trends could be clearly verified, in our national and in international studies as well. There were only a few decades’ long studies, because most of the researchers are not able to conduct studies over decades, following the patients for a lifelong period, and correctly registering their anthropometric parameters. This is the reason, why other studies with exactly measured anthropometric data covered only years and never decades. The accuracy of data and longevity of follow-up need some compromise. There are other theoretical and practical limitations of our studies such as: - therapy and interventions started at a younger age may have modified the findings, although there was not such a wide variety of drugs available for this generation, compared to more effective medications recently used, - genetic elements and other risk factors remained undiscovered, - patient’s inclusion was not standardized and those who are affected by metabolic diseases represent the largest proportion of persons attending primary care surgeries, hence they were overrepresented. Their ratio in this study was much higher than in the general population, and could not be considered as representative, although both urban and rural inhabitants were involved, - if data would be available, weight at birth, eating habits, socioeconomic circumstances, sleep duration, physical activity and their changes could be analyzed as well. More information about the time of menarche, on lactation, medication, surgical menopause or hormonal replacement therapy could also be useful, so were unable to exclude all of the confounding factors. - decade, as a threshold limit, does not always reflect biological or pathological changes properly. The same BMI does not mean the same body weight and height in different life periods. Due to the normal decrease in body height and sarcopenia , the same weight in the elderly means a higher BMI [6]. While we had data only of recent measurement and self-reports on heights at 20y, it may result some slight differences in BMI values. Comparison with previous studies Using questionnaires has been an acceptable tool for data recording. Epidemiological studies using similar method, questionnaire-reported and staff-measured weights were highly correlated. In the famous “ The Nurses’ Health Study I. and II.” and “ The Health Professionals Follow-up Study ” body weight was also questioned only, beside data on other lifestyle practices (nutritional habit, physical activity, sleep duration, “screen-watching time”) and medical history [3]. The similarities of weight gain between the control and hypertensive groups may be due to the high prevalence of hypertension among elderly. Steady weight or slow increases in body weight have been seen more often among people without diabetes. Perhaps due to the treatment, a slight body weight decrease was observed among diabetics after 60years. History of lifelong weight change may contribute to our understanding of why some obese older people are metabolically healthy but others are not [12]. Physical activity should also influence long-term weight gain, but evidence to support this expectation has been surprisingly inconsistent [13,14,15]. In addition, the duration of television viewing and of sleep may influence energy consumption, energy expenditure, or both [15,16]. In most of the epidemiological studies different lifestyle behaviours have often been evaluated separately,thus limiting relative comparisons or the quantification of combined effects. In addition, most studies of long-term weight gain have evaluated current behaviours, but changes in behaviour over time may be more relevant in terms of both their biologic effects on long-term weight gain and their translation into prevention strategies. The increasing prevalence of obesity has led to several attempts to counter this epidemic by various types of intervention. The most promising approaches are targeting children, because prevention programmes in early life are more likely to be successful and childhood obesity tracks into adulthood [17]. Outcomes of 71 European community-based initiatives against childhood obesity starting between 2005 and 2011 were critically evaluated. There were common characteristics: application of integrated actions at a local level, aimed at changing the environment and the children’s behaviour directly. Evidence supporting effectiveness on weight indicators is available, although the design and conduct of most of these studies were suboptimal (i.e. no control group, a small sample size, not random) [18]. Focusing for the whole family, not only for children, the Feel4Diabetes project ran in primary schools and local communities, considering the socio-economic status of parents as well, discovered new patients with (pre)diabetes among them [19,20]. In the past decades, after realizing childhood obesity as a real public health threat, many public and governmental initiatives were started. Most of them presented good outcomes, but usually over short time period. Besides these programmes and studies, new governmental strategies were implemented in some affected countries [21,22]. Since 2013, in Hungarian primary schools a daily physical exercise hour is a part of the curriculum. It will hopefully increase the level of physical activity of the population and decrease the prevalence of obesity. Obesity management was in the focus of some interventions in the primary care. In a regional project in Hungary, most of the obese patients lost weight, laboratory parameters improved and blood pressure decreased [23]. In other, patients were faced to their own cardiovascular risk assessment and this impression facilitated changes in health behavior and lifestyle [24]. Sustained improvement in risk factor status requires ongoing risk communication with health care providers.Individualized lifestyle counselling improves health behaviour and reduces total cardiovascular risk among middle-aged men [25]. Primary care counselling for healthy young people driving their attention on current behaviors might elicit their proactive role to improve lifestyle, getting immediate advantages such as well-being improvement and the possibility to manage stress better [26]. Changing dietary habits is still the first step in the treatment of obesity with a focus on components of the Mediterranean diet and to avoid a high intake of ultra-processed food because it has been related to an increased risk of obesity and obesity-associated comorbidities [27]. The simple obesity prevention differs from weight-loss trials. Weight-loss trials have typically enrolled obese or overweight persons who attempted substantial short-term weight loss on specialized diets, thus limiting the generalizability of the findings to non-obese populations and to the factors that determine long-term, gradual weight gain [3]. When obesity already developed, utilization of counselling was lower and insufficient achieving 5% or greater weight loss at the population level [28]. In the UK, where more resources were available in primary care, the nationwide COUNTERWEIGHT project was longer and supported by other health care professionals and by networks of “Patients’ clubs” [29]. Appropriate financial incentives were implemented to motivate the primary care providers [30]. CONCLUSIONS The patient-doctor collaboration and confidence is the closest in primary care. Family physicians are in higly responsible position in all health care systems, they should not just be inactive observers of their patients’ weight gain; they must act in due time. Instead of restrospective analyses of mass-data, we have to act earlier at individual level. All patients with visible weight gain should be systematically and individually examined, exploring the reasons and determine the type of intervention. Applying financial incentives for appropriate obesity management could be a useful tool as well. Regular anthropometric measurement should be incorporated into routine medical practice. GPs should focus also on the younger generations, who rarely visit medical settings and should provide them advice for obesity management, if needed. Our retrospective findings can initiate interventions much earlier. Primary care capacity and workforce should be linked with the global need for prevention. Declarations Statement of Ethics The partners of the international study declared that no detailed approval was necessary according to their national regulations, since there were only anonymous data and there was no intervention or influence on the patient and the respective medical care process. In Hungary, ethical permission was issued by the South- Budapest Ethical Committee (Jahn Ferenc Hospital, on 21 August 2000) and was updated by the Hungarian Committee on Scientific and Research Ethics (ETT-TUKEB 20928-I). All of the patients signed the informed consent form linked to the data-sheet. The data were processed anonymously by the respective family physicians. Data Availability Statement Data of all studies were deposited as electronic files at the Department of Family and Occupational Medicine, University of Debrecen. Hard copies of questionnaires were deposited also here and in the offices of the leading authors of the respective studies. Author Contributions: IR- conceptualization, methodology, investigation, writing: ZJ, EK, PT, CM writing, organizing their own studies and searched literature, PT, KLR contributed in the validation, investigation and editing. All authors have read and agreed to the submitted version of the manuscript Funding: There was no funding for these studies. Conflicts of Interest: The authors declare no conflict of interest. Acknowledgement Authors are thankful to the contributing family physicians for collecting and providing data. Special thanks to Tibor Deutsch, Endre Szigethy, Tímea Ungvári for the statistical analysis presented in the different surveys. References Astrup A. The Healthy lifestyles in Europe: prevention of obesity and type II diabetes by diet and physical activity. Public Health Nutrition 2001;4(2B):499-515. doi: 10.1079/phn2001136. Wadden TA, Tsai AG. Addressing Disparities in the Management of Obesity in Primary Care Settings.N Engl J Med. 2020;383(10):977-978. doi: 10.1056/NEJMe2025728 Mozaffarian D, Hao T, Rimm EB Willett WC, Frank B, Hu N: Changes in Diet and Lifestyle and Long-Term Weight Gain in Women and Men. Engl J Med. 2011;364:2392-2404. 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Wagner A, Simon C, Ducimetiere P, Montaye M, Bongard V, Yarnell J, Bingham A, Hedelin G, Amouyel P, Ferrières J, Evans A, Arveiler D. Leisure-time physical activity and regular walking or cycling to work are associated with adiposity and 5 y weight gain in middle-aged men: the PRIME Study. Int J Obes Relat Metab Disord 2001;25:940-948 Gordon-Larsen P, Hou N, Sidney S. Fifteen-year longitudinal trends in walking patterns and their impact on weight change. Am J Clin Nutr. 2009;89:19-26. Lee IM, Djousse L, Sesso HD, Wang L, Buring JE. Physical activity and weight gain prevention. JAMA 2010;303:1173-1179 Juonala M, Magnussen CG, Berenson GS. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365:1876-85. Pigeot I, Baranowski T, De Henauw S. and the IDEFICS Intervention Study Group. The IDEFICS intervention trial to prevent childhood obesity: design and study methods. Obes Reviews. 2015;16 (Suppl. 2): 4–15 Bemelmans WJE, Wijnhoven TMA, Verschuuren M, Breda J. Overview of 71 European community-based initiatives against childhood obesity starting between 2005 and 2011: general characteristics and reported effects. BMC Public Health. 2014,14:758 http://www.biomedcentral.com/1471-2458/14/758 Latomme J, Huys N, Cardon G, Morgan PJ, Lateva M, Chakarova N, Kivelä J, Lindström J, Androutsos O, González-Gil EM, De Miguel-Etayo P, Nánási A, Kolozsvári LR, Manios Y, De Craemer M; on behalf of the Feel4Diabetes-study group.Do physical activity and screen time mediate the association between European fathers' and their children's weight status? Cross-sectional data from the Feel4Diabetes-study. Int J Behav Nutr Phys Act. 2019;16:100. doi: 10.1186/s12966-019-0864-8. Manios Y, Mavrogianni C, Lambrinou CP, Cardon G, Lindström J, Iotova V, Tankova T, Civeira F, Kivelä J, Jancsó Z, Shadid S, Tsochev K, Gallego R, Radó S, Dafoulas G, Makrilakis K, Androutsos O, on behalf of the Feel4Diabetes-study group. Two-stage, school and community-based population screening successfully identifies individuals and families at high-risk for type 2 diabetes: the Feel4Diabetes-study. BMC Endocrine Disorders. 2020;20(Suppl 1):12 https://doi.org/10.1186/s12902-019-0478-9 National Institute for Health and Care Excellence (2015). NICE public health guidance 47: Managing overweight and obesity among children and young people: lifestyle weight management services. Available at https://www.nice.org.uk/guidance/qs94. Kliche T, Krüger C, Koch U, Mann R, Goldapp C, Stander V, Töppich J.(2006). Germany: preventive care for obese children and adolescents - qualities and deficiencies of programs and interventions. In A. Mathieson & T. Koller (Hrsg.), Addressing the socioeconomic determinants of healthy eating habits and physical activity levels among adolescents. http://www.euro.who.int/Document/e89375.pdf (S. 43-49). Copenhagen: WHO Regional Office of Europe/HBSC Forum.) Móczár C, Borgulya G, Kovács E, Rurik I. Could primary care dietary intervention combined with lifestyle changes be effective in the cardiovascular prevention? Acta Alimentaria 2012;41:248– 256. DOI: 10.1556/AAlim.41.2012.2.11 Ocsovszky Z, Martos T, Otohal J, Berényi B, Merkely B, Csabai M, Bagyura Z. Relationship between cardiovascular risk assessment and health behavior in the light of psychosocial factors. Orv Hetil. 2023;164:119-131. doi: 10.1556/650.2023.32685. (Hungarian) Reijo Siren R, Eriksson JG, Vanhanen H.Observed changes in cardiovascular risk factors among high-risk middle-aged men who received lifestyle counselling: a 5-year follow-up.Scand J Prim Health Care. 2016;34:336-342. doi: 10.1080/02813432.2016.1248649. Lucini D, Pagani E, Capria F; Galiano M, Marchese M, Cribellati S, Parati G. Age Influences on Lifestyle and Stress Perception in theWorking Population. Nutrients. 2023,15,399. https://doi.org/10.3390/nu15020399 Gómez-Ambrosi J. Recent Progress in the Management of Obesity. Nutrients. 2023,15,2651. ttps://doi.org/10.3390/ nu15122651 Henderson J, Ehlers AP, Lee JM, Kraftson AT, Piehl K, Richardson CR, Griauzde DH. Weight Loss Treatment and Longitudinal Weight Change Among Primary Care Patients With Obesity. JAMA Netw Open. 2024 Feb 5;7(2):e2356183. doi: 10.1001/jamanetworkopen.2023.56183. Counterweight Project Team, McQuigg M, Brown JE, Broom JI, Laws RA, Reckless JP, Noble PA, Kumar S, McCombie EL, Lean ME, Lyons GF, Mongia S, Frost GS, Quinn MF, Barth JH, Haynes SM, Finer N, Haslam DW, Ross HM, Hole DJ, Radziwonik S. Engaging patients, clinicians and health funders in weight management: the Counterweight Programme. Fam Pract. 2008;25-S1:i79-86. doi: 10.1093/fampra/cmn081 NHS-QOF. Available at https://www.england.nhs.uk/wp-content/uploads/2021/03/B0456-update-on-quality-outcomes-framework-changes-for-21-22-.pdf Additional Declarations The authors declare no competing interests. Supplementary Files APPENDIX.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4426620","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302915323,"identity":"aea7d14e-d53a-4675-86f5-1c02624c5bc8","order_by":0,"name":"Imre RURIK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3OMWsCMRTA8ScHcUl1jZz2vkJEqP0uLieFc7mjq1N707nonkI/hNObL2S1eyEdWg5ucrjDxQ4FQwrdTs+tQ/4khBf4kQC4XP80mQIzh5fbqWd2btdlQkI7kTakk9qD8j8C58hUJF9SwP1sulofqjrGW8IepDzCxzxtIMP3BZdbYMnr7g0HL6gnhEWholA2EsYikJ+GCJagd4N6nvkxVwCqBQn2xeEH9XPmP1bmYxfI1r5Cwe+gDokfQ07PEVqCFNwQGt0NNqjHWVByRbmaNJJu5NXr5VMiuqqovlEHfaqK+rhUoybyG29x43K5XK4rOgHTVV4sugU1rwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2818-8714","institution":"Semmelweis University","correspondingAuthor":true,"prefix":"","firstName":"Imre","middleName":"","lastName":"RURIK","suffix":""}],"badges":[],"createdAt":"2024-05-15 17:12:31","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4426620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4426620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56973629,"identity":"817b203e-69ee-458c-a839-d9f9ef8c939a","added_by":"auto","created_at":"2024-05-23 01:43:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68473,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of body weight over life-decades (DEC) and in the last decade before diabetes was diagnosed [kg]. Data of all persons without diabetes were compared to all patients with diabetes alone or with hypertension [11].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4426620/v1/19c03e8f05ab76a755bded7d.png"},{"id":56973631,"identity":"55a1f75b-daa0-4d06-ba00-3c977e3218aa","added_by":"auto","created_at":"2024-05-23 01:43:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1127885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4426620/v1/d714c4b9-39f6-4107-b640-bd54e6125caa.pdf"},{"id":56973630,"identity":"0ad78924-2a17-4d37-b9b1-4b856e805681","added_by":"auto","created_at":"2024-05-23 01:43:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15978,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-4426620/v1/a2fe8aa6141e4e28edb8e5f4.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEarly Obesity Counselling in Primary Care Setting Could Decrease Metabolic Diseases Are Diabetes and Hypertension Avoidable, if Stopping Early Weight Gain?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe prevalence of obesity is increasing rapidly in all age groups worldwide. It is currently one of the fastest growing epidemics, affecting 10\u0026ndash;40% of the adult population. Reduced life expectancy and obesity related complications may account for 5\u0026ndash;10% of all health costs in the European countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Obesity increases the risk of adverse health conditions, the risk of metabolic and cardiovascular diseases, certain type of cancers, impaired quality of life, and (as recently discovered) complications from Covid-19 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the normal ageing process, weight gain often occurs gradually over decades (about 0.4\u0026ndash;0.5 kg per year), making it difficult for most people to perceive the specific causes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prevention of obesity is not always easy; there are some essential contributing factors, i.e. genetic background, lifestyle, medication, environmental circumstances etc. Since weight stability requires balance between energy consumed and expended, in most of the cases, the advice to \u0026ldquo;eat less and exercise more\u0026rdquo; seems to be straightforward in the prevention [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It should be started as early as possible; therefore interventions preferably in childhood could be implemented for prevention. Obesity management requires holistic approach, analysis of individual risk factors and it is a time consuming, not always successful process.\u003c/p\u003e \u003cp\u003eExcess body weight during childhood is associated with an increased risk of adult type 2 diabetes only if it continued until puberty or later ages [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition to the robust evidence that obesity and excess body weight are strongly related to the incidence of metabolic diseases (type 2 diabetes, hypertension), the rate of weight gain could also contribute to the occurrence of these morbidities. The rate of weight gain was evaluated in studies among middle-aged and elderly populations over a few years of follow up periods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. None of the primary care studies focused to this phenomenon and it was the reason why our research started, initiated in the primary care setting, the first line of the health care system. Our first Hungarian lifelong study, in which the two main components of Metabolic Syndrome were evaluated [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and the following others found differences between the dynamics of weight gain among patients with obesity, diabetes and hypertension [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA medical setting is required to control laboratory parameters (plasma glucose level, lipid profile), blood pressure is often measured by the patients, while body weight and height are usually checked by the individuals themselves. Although many people regularly measure their own body weight, remember these data, they rarely store or compare these records.\u003c/p\u003e \u003cp\u003eThe aims of this paper are to present and summarize our previous findings; how the weight gain trends to overweight and obesity in the younger decades and could be responsible for the onset of metabolic diseases and to explain why early primary care intervention is needed.\u003c/p\u003e"},{"header":"METHODS and PATIENTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA cross-sectional survey, combined with retrospective analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSelection of participants\u003c/h2\u003e \u003cp\u003ePatients (aged between 60 and 70 years) who visited the offices of their family physicians/general practitioners or primary care outpatient clinics within the study period for any reason (having actual complaints, coming for regular screening, drug prescription, etc.) were \u003cem\u003econsecutively\u003c/em\u003e selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eHaving been informed about the aim of the survey and given the proper instructions, the selected participants were asked to fill in a short one page questionnaire in the doctor\u0026rsquo;s office. The form contained questions regarding self-recorded data of body \u003cem\u003eweight\u003c/em\u003e in each decade of life (at 20, 30, 40, 50, and 60 years of age), the most recent and the highest values; body \u003cem\u003eheight\u003c/em\u003e at 20 years and recently. For women, the weight before the first pregnancy, after the last delivery, before and after menopause was also requested. Primarily, if were available or recorded previously, data in the medical files were used, otherwise the self-reported data were considered. The recent corresponding measures were performed by the staff and they added the time of diagnoses as needed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the international study, the questionnaire in English was translated to their respective national language by the family physicians, who participated in the national arms of the study in Germany, Hungary, Italy, Slovakia and Ukraine [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. (See \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e\u003cb\u003eAppendix\u003c/b\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe protocol of the first Hungarian study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] was continuously improved using more developed methods in order to drop out unreliable data. Questions regarding pregnancy and menopause were added in the last (international) study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSettings\u003c/h2\u003e \u003cp\u003eThe participating practices were located in the suburb of \u003cem\u003eBudapest\u003c/em\u003e, in \u003cem\u003eDebrecen\u003c/em\u003e and \u003cem\u003eKecskem\u0026eacute;t\u003c/em\u003e (Hungary), \u003cem\u003eLeipzig\u003c/em\u003e (East-Germany), in \u003cem\u003eRome\u003c/em\u003e, around \u003cem\u003eNaples\u003c/em\u003e (South-Italy), in \u003cem\u003eBratislava\u003c/em\u003e (Slovakia) and near to \u003cem\u003eUzhgorod\u003c/em\u003e (West-Ukraine).\u003c/p\u003e \u003cp\u003ePractice sizes varied between 1200\u0026ndash;1800 enrolled patients, 56\u0026ndash;112 subjects participated from each practice. The duration of the study was usually one week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria\u003c/h2\u003e \u003cp\u003eThe primary exclusion criteria were: serious visual, acoustic or mental impairment, participation in our previous studies or simple refusal to participate. In order to reduce methodological bias, the validity of self-reported data was checked. After completing the questionnaire, the participants\u0026rsquo; current body weight and height were measured and subjects (approx.20%) were excluded if the difference between the self-reported and the recently recorded data exceeded 3 kg or 5cm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative variables\u003c/h2\u003e \u003cp\u003eThe participants were divided into four groups: patients with diabetes (\u003cem\u003eDM\u003c/em\u003e), with hypertension (\u003cem\u003eHyp\u003c/em\u003e), or both (\u003cem\u003eHyDm\u003c/em\u003e) and a control group (people having neither diabetes nor hypertension). For another comparison, patients with both illnesses were counted among diabetics.\u003c/p\u003e \u003cp\u003eAfter weight and height measurements the body mass index (BMI) was calculated. Percentage of weight change was calculated as weight at examined decade/ weight at previous decade. The overall incidence rate was calculated as the ratio between the numbers of cases. Hypertension and diabetes were used as an outcome measure. Independent variables included gender, age and BMI categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eIn the different studies, the following statistical methodswere used: Student t-test, logistic regression (OR and CI), ANOVA, Fisher and κ\u003csup\u003e2\u003c/sup\u003e test, Cox-regression model, Sch\u0026ouml;nefeld residual test. The analyses were performed using \u003cem\u003eSPSS\u003c/em\u003e and later on \u003cem\u003eSTATA 10.1.\u003c/em\u003esoftwares.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAltogether 2 468 persons, above 60 years participated in the four studies. Their distributions regarding referenced studies:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e354\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e540\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e815\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e759\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWithin the results of the studies, there was no statistical difference between the mean ages of the established diagnoses. Diagnosing hypertension was 55.4 (\u0026plusmn;\u0026thinsp;15.9) (median: 59) years in men and 53.8 (\u0026plusmn;\u0026thinsp;15.7) (median: 56) in women, while diabetes was diagnosed at the age of 51.3 (\u0026plusmn;\u0026thinsp;17.1) (median:53) years and 54.4 (\u0026plusmn;\u0026thinsp;13.1) (median: 50) years in males and females, respectively [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Higher age at the recognition of diabetes 58.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 years was recorded only in one study and only by men [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFive hundred forty patients were involved in our second study [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. The most important phenomena, the faster weight gain in the early decades by men with diabetes and hypertension were demonstrated on Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. and by women on Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeans of self-recorded current and maximum weight [kg] and weight gain between decades [\u0026Delta;], and last decade prior to diagnosis \u003cstrong\u003eof men\u003c/strong\u003e, with hypertension (\u003cstrong\u003eHyp\u003c/strong\u003e), with diabetes (\u003cstrong\u003eDM\u003c/strong\u003e), with both (\u003cstrong\u003eHyDm\u003c/strong\u003e) and with neither disease (control group-\u003cstrong\u003eNone\u003c/strong\u003e ) * Significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) difference between decades of different diagnostic groups [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHyp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHyDm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e: \u003cstrong\u003e225\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e104\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge decades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e66.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e79.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*6.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e70.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e2.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e2.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e2.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e89.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*5.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e79.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*4.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e77.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e5.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLast prior to diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeans of self-recorded current and maximum weight [kg] and weight gain between decades [\u0026Delta;], and last decade prior to diagnosis \u003cstrong\u003eof women\u003c/strong\u003e, with hypertension (\u003cstrong\u003eHyp\u003c/strong\u003e), (with diabetes (\u003cstrong\u003eDM\u003c/strong\u003e), with both (\u003cstrong\u003eHyDm\u003c/strong\u003e), and with neither disease (control group- \u003cstrong\u003eNone\u003c/strong\u003e )\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWOMEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHyp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHyDm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en: 315\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge decades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Delta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e54.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e54.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e58.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e2.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e2.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*10.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*4.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e65.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e4.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e64.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e4.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e66.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*3.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e77.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e*3.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e0.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e78.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e1.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e4.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLast prior to diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e3.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e6.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e6.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) difference between decades of different diagnostic groups [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn the fourth study [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] a lifelong body weight gains were observed in all diagnostic categories and genders (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cstrong\u003e).\u003c/strong\u003e The last measured data were usually smaller then the \u0026ldquo;peaks\u0026rdquo; within previous decades.Diabetes and hypertension were diagnosed earlier among patients with both morbidities, while hypertension alone occurred later. Diabetic men reported their highest weight gain between 20-30y of age, when expressed both in [kg] and in \u003cem\u003epercent of the weight at 20y\u003c/em\u003e. It was usually followed another fast weight gain in the last decade before diabetes was diagnosed. The highest early increase was recorded among men with both morbidities. Patients with hypertension had higher, but not always significant differences through decades than the control group had. These changes were more markedly reflected in the figures of BMI (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cstrong\u003e)\u003c/strong\u003e. Similar data of women are presented on the Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. and \u003cstrong\u003e6\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Men\u003c/strong\u003e. Data of recorded \u003cstrong\u003ebody weight\u003c/strong\u003e (means and 95% confidence intervals), their changes (\u003cstrong\u003e\u0026Delta;)\u003c/strong\u003e by decades (in kg and in % of the weight at previous decade). Mean ages at the time of diagnoses. Abbreviation used in the table: \u003cstrong\u003eDM\u003c/strong\u003e-diabetics, \u003cstrong\u003eHyp\u003c/strong\u003e-hypertensive, \u003cstrong\u003eHyDm\u003c/strong\u003e-patients with both hypertension and diabetes, \u003cstrong\u003eNone\u003c/strong\u003e (control-patients) free from these pathologies; s:significant, ns:non-significant. [11].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e.\u003cstrong\u003eMen.\u003c/strong\u003e Data of calculated \u003cstrong\u003eBMI\u003c/strong\u003es (means and 95% confidence intervals), their changes (\u003cstrong\u003e\u0026Delta;)\u003c/strong\u003e by decades (in kg/m\u003csup\u003e2\u003c/sup\u003e and in % of the BMI at previous decade)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviation used in the table: \u003cstrong\u003eDM\u003c/strong\u003e-diabetics, \u003cstrong\u003eHyp\u003c/strong\u003e-hypertensive, \u003cstrong\u003eHyDm\u003c/strong\u003e-patients with both hypertension and diabetes, \u003cstrong\u003eNone\u003c/strong\u003e (control-patients) free from these pathologies; s:significant, ns:non-significant. [11].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Women\u003c/strong\u003e. Data of recorded \u003cstrong\u003ebody weights\u003c/strong\u003e (means and 95% confidence intervals), their changes (\u003cstrong\u003e\u0026Delta;)\u003c/strong\u003e by decades (in kg and in % of the weight previous decade) Mean ages at the time of diagnoses.\u003cem\u003e\u0026nbsp;Abbreviation used in the table: \u003cstrong\u003eDM\u003c/strong\u003e-diabetics, \u003cstrong\u003eHyp\u003c/strong\u003e-hypertensive, \u003cstrong\u003eHyDm\u003c/strong\u003e-patients with both hypertension and diabetes\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eNone\u003c/strong\u003e (control-patients) free from these pathologies; s:significant, ns:non-significant. [11].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Women\u003c/strong\u003e. Data of calculated \u003cstrong\u003eBMIs\u0026nbsp;\u003c/strong\u003e(means and 95% confidence intervals), their changes (\u003cstrong\u003e\u0026Delta;)\u003c/strong\u003e by decades (in kg/m\u003csup\u003e2\u003c/sup\u003e and in % of the BMI at previous decade) Abbreviation used in the table: \u003cstrong\u003eDM\u003c/strong\u003e-diabetics, \u003cstrong\u003eHyp\u003c/strong\u003e-hypertensive, \u003cstrong\u003eHyDm\u003c/strong\u003e-patients with both hypertension and diabetes, \u003cstrong\u003eNone\u003c/strong\u003e (control-patients) free from these pathologies; s:significant, ns:non-significant. [11].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003eThe tables demonstrate that both men and women who became diabetics later on had higher body weight and BMI at the age of 20. For diabetes, the highest increases were reported in the third, for hypertension in the fourth life decades and the increase also continued in the fifth decade. For most of the affected patients, high increase was recorded in the last decade prior to diagnosis. No differences were found between countries. After 60y, a small decrease was observed by both genders in all of the studies. The differences between weight gains of diagnostic groups were more visible, when diabetics and patients with diabetes and hypertension were merged into one group and were compared with the control group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.).\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the third study [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], patients were divided into \u003cem\u003equintiles\u003c/em\u003e (N\u0026thinsp;=\u0026thinsp;127\u0026ndash;141) according to the distribution of data of weight changes between the respective and baseline decades, as seen in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Data of other quintiles (q) were compared to that of \u003cem\u003eq1\u003c/em\u003e and between genders. There were lower differences between data in the first decade therefore only three groups were formed here. Incidence rate (IR) and hazard ratio (HR) of both illnesses were the highest in the group \u003cem\u003eq5\u003c/em\u003e where the largest increases were registered. HR was even higher when both hypertension and diabetes were diagnosed.\u003c/p\u003e\n\u003cp\u003eHigher increase in body weight also resulted in the changes between BMI categories. Patients who belonged to the \u003cem\u003enormal\u003c/em\u003e BMI category, had 2.80 odds ratio (OR) (95% CI: 1.24\u0026ndash;6.25; p\u0026thinsp;=\u0026thinsp;0.013) for diabetes when changing to \u003cem\u003eoverweight\u003c/em\u003e and 3.23 OR (95% CI: 1.19\u0026ndash;8.76; p\u0026thinsp;=\u0026thinsp;0.022) when moving into the \u003cem\u003eobese\u003c/em\u003e category.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 7.\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u0026nbsp; [10]. Decades long weight changes by gender and co-morbidity (in quintiles). \u0026nbsp;IR and HR for hypertension and diabetes . \u003cstrong\u003eDec-\u0026nbsp;\u003c/strong\u003elife decades of patients \u0026nbsp; \u0026nbsp;\u003cstrong\u003ey\u0026nbsp;\u003c/strong\u003e-year. \u003cstrong\u003eRange\u003c/strong\u003e- upper and lower values of quintiles with equally distributed data (N=127-141 at age 20y), -calculated from weight at respective decade/ weight at 20y. \u003cstrong\u003eIR\u003c/strong\u003e- incidence rate of the respective morbidity (expressed in cases per 100 persons-years) \u0026nbsp;(relevant IR for Male* is the same for all decades). \u003cstrong\u003eHR\u003c/strong\u003e- hazard ratio of developing morbidity (diabetes or hypertension) adjusted for gender, age, and co-morbidity. \u003cstrong\u003eCI-\u0026nbsp;\u003c/strong\u003econfidence interval. \u003cstrong\u003ep\u003c/strong\u003e- level of significance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-morb (\u003c/strong\u003eCo-morbidity\u003cstrong\u003e)\u003c/strong\u003e- \u003cstrong\u003eNo\u003c/strong\u003e: diabetes or hypertension only, \u003cstrong\u003eYes\u003c/strong\u003e: both are present (relevant IR without *co-morbidity is the same for all decades)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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IiyasfXLHTV3wymMJuQe9t4VU69n+5aPqS3uuOfyCMCcqbUt6WDV8Z+ONk0V+9NZ7E/7w/40Hp6SNjuDqRWCRRwhLqu73uZX12Zc9KtBY8zm2J/M0nHAaHYGD0wTHHL3FBd+eccxs18I4GUDRkNyz26ZcYjPhuEzelbOTBf703SxP+0GT0kvHOv3hKnrwXZaLku0rf3JjIX9aTw8wmiMMcYYY4o4YDTGGGOMMUUcMBqzAPg1+pKe0+Edc/t8gbMxkSX4U99XZIwZigNGcwYaydjQ8GwFS1fjw7HYsKbnm93DA958C1c/JCLYKtktHo/2i/v1UuAIAVxufx+cF6+nB9LVKcdjWnjlEi+RJ40x+2Rq/sTLuGP+LOzjOpyPTOlx9vPjQtaN2Rr/jMwERFg0Q/Srl8HqBbC88JQXv7LNfr14VrDNS2BT9GLY3Itkx2Lq9Wxd+bBZfBmtXrArOIYNI6kdBedxfpoHsG+dl95ybfKK11T+8Rq5dNBVx+ZEqsslMPUyrSvf1PyJNJybni9/kW90peM6m7wAfYqkZVwCcynT6FIu0fhTola/atT4L9hWY8N6bChpqLoaSuBY2rCOydTr2TryKZCPoPe0I8IOsiv/cwGY8uJ/XBfrdDr6wkLXuXG/OsC0TuXKODfmLn+OqZdpHflydW1MfyJfzkvPFRzXtbsCRuBaORnnSq6Mc2cuZfKUtGng26mrhrDq/YhMhzx+/Lj5xmgXfK9z1chVTbmY9eAbsqsOrd06Aduk3wNnyurjx4/NOnb+/ffff5qq0pcQ+IYtC2gfU3RDv2DBdNnbt2+bdepKjpo8kYF66Xpkds3U/El+g0w6N8JjGzVfy+KF4kxPG7MpDhhN0yjSud+8ebPdc8LqzvTo77//bo6DjvNcGQ0Q+/XMjJ5LEwo83VDtDl4WfPHixXbrBDoHvq4jmwE2FAT5qxvFo6dPnzZ2iz8sefXq1dG1a9eahXUgUOsK+EqoQ4Vz5861a+tBvfv69Wu7ZcxumJI/cT1uuCGVaShXrlw5I7Mx6+KA0Rx9+vSpXTsLd8E0WozwEDzSONIg0ogSENKRs580BJyxsRRuqPYLX5vAJthMwTz2odMAjVSQ7vj4+OjRo0ennSGjFXR+LKyz/8OHD42tWVd+OTvvmnSUx5h9sBR/UvBpzCY4YDRF1OARPNLIvXjxomkcWVcjpEaTO/QUN1T7B1vJbox8dD1qwD46Q02ZpTCSrMcOuDlglAR7xk6xiwsXLrRr3TckxsyBMfwpTkHn2lVjxsABozk6f/58u1aGRu7du3ftlhmb3HNNETohOiOesyqRmzKOnRvQsVFPdM2+IJARFTpWYFQlh59LNFNiav5EcAp6FjiF54T7btyM2SYOGE3VM2Y0eDSUauD4T0AQn3FkqjqFO26zG3i2qWv0gWAM+zCKQfCWgzTYLO0o6Yh4D13cT17fvn07tfXly5eb/yV0c8EzYOm0W/rMawnyQR5jdsnU/EnT4ZC+/5Frxfa4BM8Tux02W+GfkZmACIumVr+rBqXzNTirO93O10CQP0t6nNdNsH/VwLZ7xmXq9Wwd+T63r+uIYCv25fJTei3pK2wAu+VsHc/lGkPgOvG6LFwHVE/SReTKODfmLn+OqZdpHfmm6k85H4nXot1Oj8d2l+tPpR3eBpRvacylTL/wZyXsaPDQ78giLJpa/XIXzK/5Vo1T9vmcoTAiycjQVH70MvV6tq58GrljNGKJUI8Yneka1ZkDS2zj7E/zgHadkcia1/jMBfvTeDhgXDhD9EtjyTM6m9pD+azuoqumTPbB1OvZJvIxvctU2NKCRuoRv46Oz37NkSW2cfan6cN0N1PfrnvTZy5l8jOM5hQaSEYYc99LrYU7Wn5JTeWfSrC4dBg9ILBC90uBZ7Rg7sGimR9L8Sd+pLi0wMqMyyRGGI0xxhhjDpU5BPeekl441u8JU9eD7bRclmhb+5MZC/vTeHhK2hhjjDHGFHHAaIwxxhhjijhgNMYYY4wxRWYVMPJONub6teTgl219abrgdQo6r+uXwjGN3tlljDHGGLNkZhMwEizyibFIGhASLPLy6adPnzYPkN66datJU/O9TQLE+M1O3iGYfr6MbdKQN6+f4V2DyGWMMcYYs2RmETASCBL0EagpEBRxlO/u3bvN/ytXrjT/tf3s2bPmfxfkwdvwlT8vO4UYQPJeOLZ1bb6GQjqC2JqA1BhjjDFmrswiYDx37tyZTxvlXuZL0MaoIJA+wifqSty+ffvMp8d44SnEwPTDhw/N/9zLqD99+tSuGWOMMcYsj1kEjKUvhmg0sRS0EUiWRgFj/oxmauo7BqmloJNgMn2+UtPZcb9HIo0xxhgzR2b1oxfx/fv35j8jgEwNbwuCPJ6BFEOeT2TUM45IKthk//379yf1XWVjjDHGmCHMMmB8+fJl83/b35klyOPHLIKRRn3TtoY4IhmDzYsXLzpYNMYYY8xsmV3AqCnjV69e7SQIY8SSH74IPbtYC7/QBv0Yhh/U8IykMcYYY8xcmV3AqNfmxB+pwOXLl9u1n+HXzASXvDonPmdYeq5QgR+jg6AfwuS4fv16u3Z09PDhw3bt6Oj169fNf48uGmOMMWbOzCpg5BlDArkYlOm1OgRleh2OnnEUCvb+/vvv01fnaOE8Asf0nYsXLlxo/utHNQoKcwFmGqzyzCLwnkadb4wxxhgzW1ZB06jUirAKwpq06XLr1q02xT//HB8fN/tWQWWzzTG2P3/+3Gzn4Fgur1XweZqPUH6gayFXivIkj7GRvIfO1PVgOy2XJdrW/mTGwv40Hr/wZyXsaDC61ycCPyDRq25S0hFHfe1F1BSPUUpGAyNdv2rW114gvXYEmRmVTKfO902Nfg+BqevBdlouS7St/cmMhf1pPGYRMM4RAsZt/4p7HdxwnjB1PdhOy2WJtrU/mbGwP43HLF+rM0V4thGj63U6+rGMMcYYY8zcmcUII2mWwBiqXuLd2DpMXQ+203JZom3tT2Ys7E/j4RFGY4wxxhhTZBIjjMYYY4wxh8ocRhj9o5eFY/2eMHU92E7LZYm2tT+ZsbA/jYenpI0xxhhjTBEHjMYYY4wxpogDRmOMMcYYU2TRASPPBehb0338+9//btKz8LWYQ0a60DslI+hTeloylK+v7ujdm4egD2PWhXZEPpL7Fn8OfC/9vr8xZlwWGTAS8A3pwGmY3r171zx0+urVq6O7d++2Rw6Lv/76q9HbvXv3Gl2kX6rh2IsXL5pjS3voWAypOzdv3jzVxf3797MBtjGHDIEfQSI+wudWL1261B7pBh9MP9VqjBmfRQaMV69ebRqomsYJCIz03ejLly83/w8NGunff/+9adRz38dm1JFvZ//999/tnmVSW3foBKMu+G64MeYsBH6PHz9u1mljubHqG7l/8uRJk84YMy1mFTCm08aMiG0DBYvAqNGff/7Zbh0ONNIEhARK6DdOB6FndP/+/ftT/ddOLS2VWGfQBcH2FL4dbsxUoI0GbsIEn0ylHemCdufNmzftljFmSswmYCRg0VQpo2DXrl1rj2wHPZtH3ocGAc/bt2+Pvnz5cqpftjUSoACakQKO37p1qwmszUkHVzuSbcwh8e3bt59848KFC52zFLQ3Go00xkyPWQSMGknUVCmjO9vupMmbYIipEIJR3R0fAp8+fWr0qREyTR3xvCIQPNKQa6SAdYLKQ9JRF4yGaAqbGw5jzHByo5HGmGkxi4Dx69evzQjjpjCSpilVltyv8Aia6Py5Oz5kSs/kxUYdHUadHupUNT+agkOfqjdGnD9//qcZm662nEdieN5R7cjz58+bG1XWjTHTYDZT0tv4sQUjZ4wGael6VmYbwemc4Ic+NOxpsKNpZ6agP3782KxHCBw1wqYlPtt3SBxquY3pQjeWsV3hsZcbN260Wz9I2xFmOGh3WDfGTINZBIy3b99uApq+X9dtA6ZGCE5/++23ds/yIdihcY6vheFHHHfu3GnWec0Qd/9q+EnnXzGehbqJThw4GvMDfkj37NmzZp22lZHD3FsYjDHTZxYBI50w70eMUxbpVEeEwEZpOCc39SyUVgtTI0t/dUwOjbZKD+hbIwQEz2zrOT10ttRfBJfqjl5ATBq9s1IL+FfSxpxFwSE+wrPhsd2O/mSMmT6//DPymD8NxjoiMG1McHdII4HrsK5+l8bU9WA7LZcl2tb+ZMbC/jQes3mG0RhjjDHGjMMsA0ZGF5na4Dm7bb282xhjjDHG5JntlLSpw/o9Yep6sJ2WyxJta38yY2F/Go9JBIzGGGOMMYeKA8YKfCe4W6zfE6auB9tpuSzRtvYnMxb2p/Hwj16MMcYYY0wRB4zGGGOMMaaIA0ZjTDW8yPwQXrR8aJ8HNeNgfzJzYvEBI59sK33pxZyFZylyn2Dks14c6zvO/yXQVc6U9Isv+yp/rXzQlRa/kNw1r6ei0X/8+PFPnz9UHn2dQtRVLq2+/MFSo0d9lacmfWonddIxj7i8e/eu+b/vzjzqoPba2DZt42I+rEf67LBtassU02nZtXyp/ft03leWmFeq95Quf5Jf9p2P3UvX0jGWGn+K+fWl70ublmEO/lRKSxl1LG1Loz8tPdZYdMCIkfm8m+lHDpED59FnvXgw9/3792cCDByI40ugpIccHz58aHSiRZ9T3BVD5CulpXGks0Lm4+Pj5p2mJUjPl5XS8pE/n40kn5s3b/7UmArqENdQHbp3795pZwKsk0by8P3yPuhoSQ99ev/69WuTVos66devX5/Zz/fAKQ/HkZUy7Qt0Jx1wbT7F2UeujYv5sLAuXZM+1lnYZSdXWybSXL9+/VQuFr5DTT3ZJdhX18P2JZ33lYU2kbqr/EqfCu3yJ+AzrciCPrpQ+ytZ+EY3thXYFP3peF/7rHNJz3nI1kVN2rQMU/enUlr2d/V/qT/xWeHYNy6OVSFHZZci3Lp1659VpW3+HypD9btylH9WjUC7dQI6ZBGrRrFJF1k5U3Mtjk2RbeghhbKugot2azN2IZ/IpcVekdL1c/aG1Le4RqwnkTQPtpWWda4vmVSXakD/XdcU5NdlJ64tSJeWk7xr9dxFbVlIF+WpuXaujaMMabkkQ6oHtmvli2y7TLJ9hHLk9tdQI1+qC+kpyhvpKwvbNZBHzp8iffKnesH+sTxpHei7XsyPfEplqU2bK0OX/YdQY1vos1eklJb1WMZov9QOpEvrVQ21ZRqbWY0wMoTPiAYLkX0pkucOi7scszlMJ8S73XPnzjV3W9x5HTLcWTNqVjviNyXiNBi+smrk2q2fefnyZXakh1GNmpFAYCSFOiOfJc8HDx6crq86vDMy1cLdfWkkBhhFlJ3SNiOO8DDq8+eff7ZbJ5D3ixcv2q3doVGbKM/Fixeb0Ywuuto49Pzt27d264etuUb67f3z588XR142YUiZUttzLu39OnWillQXuhbtW0pfWahX+EOujqV0+ZPgWvhDiVQvjGxdvny53ToZOdXIH/n1jdQqP3wAnyqNjtak7SrDFP2pL22p/4t2wO7sS+vVkphNwEjjQaVfBbmNsUpD7AwvM91mtgP6pmMRcpLv3783/w8VGkrq4+pOtOkoqHdzggYOud++fdvuyUODeeHChXbrBHWKsXEsBTewujM/DdxogFWP0sDz06dP7Vo/nNvXQN++fbuxE4FR19R7rtMAOmHq/64hwEsDN3ROIJCj1MbRUcdjpRu7jx8/FqcfN2FomSLIdePGjXZrP1AH7t+/fyYIEH1loZ6ob6KOlZ69zPlThICy9kYMkJu+McpN24Rs+Br2ffjwYXskDzcfpMWf+m7AatJ2lWGK/tSXtqb/Qx9dbcuSmEXAqM5JlR6DpQYWXQ2/MdtGDQf1kmCIZ8nmNOpKoKWAl8aub2QkwshCOoJA4NnVgdCB0nGhJ1ADLH+NoyO5vHNwbk062YmOGnI2onMrjapMya59bRyjjnRydGIsaitz6QnypzgiwigUgf4+oX6W6kAJ1TH+41Ow7g0k9TT6Qx9pQMh1sTv5YHvJVoI603dTJWrS9pVhTu1kDbpZoA0koF4qswgYeWC9dMcWwXnouNVYcheEEVk360HDwF2YkLPnpm4iGsHSMrcRuCHQGRO8zHHUlc4G2fGzIcQRoNyIo6ABpQOho0FPBKj4qUg7NY049tWfoaNQXR0n+aYjNPuGADodeelq92raODowLdiWkbOUrintbTGkTBECYtLs0x7UgdKs1NCyYKMvX760W/Woba0tO9ePNpQfYnfywO+oH+Rb0x6THmoCuq60Q8uwC4bYqy9tbf9HeXm0p2YEfa7MZkq61gi6+9FCQ0mDybpZD56HwYEEQVHayefQCJaWvmmRJdAXRE+Vki27OsUIz/7lghIaV3w35l+ajouBZ1/9GToKhSy0BWlZCb7Grpsa/YudLwFHLiAe0sahT4JJPS8qeP5s14/tDClTZOiU7KaozuVGYMU6ZeE5uBwlf+J5277nDQX5KGgT+OGVK1farbN+XdMek76mbYeutEPKsCuG2Ksv7ZD+j+Bz0awqzqjUiLCK/pt08RdOK4NV/Rpp1Zj6V9IDQK9RzyD9i5zulSb+0mxKbEMPJdDHkPQpu5SvL21q3xR8iCVCftrHeun8tF7E+hOvrfUavya/Pr8mn5iG63KNCGUoXY9jnLcJJd1Eok4pX+15nJPTBfvJo6/MHK+tS2KXZarNu0RtHuhB8ol0WwwpS6nOkEfXNXJ1NAfXVjrKIP9K80bmIf0f6Uv+EOlKWyoD6afoT6W0lCVuI3+XjtB1rf4itWUam9GlrFUURiBtXGoMQyUY4jBLo1a/cgotqc7kRCw4VyS1TXp8CiBXDSU9UJfYR5o0XWyk14E8aqiVD0pp4/6+Bhzb59KQn/KIkDbqI5Uj9dtYf2p8mvyVPpZJdZT/cVuL9CLY7is75di0PnPtWmTDVN7UthGORT3ILnGfiLqLSy7fEpxTy5AyYX/2b0qNfFGuuKgO5uTrKkv0BZYSOX+ijsXzY97Rn9I6zZLaOcqSqwOR9Lqxrqf+VEoLpTIIypGeNxTyrqXLXkNsC1HvUf54Dot0NRTOnQO/8Gcl7GjwLMU6IjAcz3MiuWemzA/W1e/SmLoepi4fU5j8oOWQ/I0pKqajNn0maYk+aH/aDPvT+tifxmNW72E0xowDvx7lmTc973UIXLp06adnxIzZBvYnM0dmGTAyuvj588n7rg7J4YwZE0YGeKg+Phy+VGhjuOOvefjfmHWwP5m5MdspaVOH9XvC1PVgOy2XJdrW/mTGwv40Hp6SNsYYY4wxRSYxwmiMMcYYc6jMYYTRU9ILx/o9Yep6sJ2WyxJta38yY2F/Gg9PSRtjjDHGmCIOGI0xxhhjTBEHjMaYan799deDeQ2IMbvG/mTmxGIDRt7PyHMBWv73v/+1R0wJdPXf//633foB+pMuc8eBhk9pat6PmcsrXicu0X5Ks0ubkn9XOXOQlsZ/X9TIF+3BklJj0wiNPi8bTt+lpjz6OoV4vZyu+PpFbV4Qy1eqC5RN6bRwLZGTixcMs73vzjzqoObapMvZrta2lLfG9pswpExjtdtcq08Pff40tP5u6k9RV2naPllzkIfS9+k9+lSaNh6Tn03dn1J95dKSV84m0ddYlsxiA8YPHz40D5FquXr1anvE5FClz4HzXLt2rXlZOrp8//79TwEhzsSb/I+Pj5s0pU9ela718ePHM3bjmuQr+9EYIcuuKMnWBec8evSo3dotQ+TjM1zS4/37908bb6ixaYRz+RRn6kfI8urVqyYPrtfV6SJ3vB6kaS9evHgqb83nw+holVeffytfpedLG9AlF3mzjzLtC66LXZBD9b6LUj2otS3Xe/v2bbu1G4aUCfbdbm/Ln9Dv8+fPT48TWMTjKZv6EzrlwxWy8b17985cL8p669atoizAjQNBHemfPn16dPfu3fbIz6AzUFrKIVTPOIZs6IT0U/en1LZpWuxJWVJSX8N2tfVplqwKOSq7EIEPgOsD8ofOUP2uHOWnj8PzgXUWgX5JJ0gft2vJXWvleO3aCRzPpaFcQz70vg09dMEH/tFP34f+S2xbvlSP+EO0YZ9NI13H0jIjT8wzwvnRJ9O0HEtlriEtV0paR7hu1FufXKzH9OtQa9u0TtdcO1cPOC+WIWc/9nFe7vwadlEm0kVbbMK+/YlzU1/oag9y9gDyq/WnNA+2lTYna1/bFM9hvaS/mDbVQ3ptrhttStqSnmvYdt1L65zKH88Fzk3tlrNRWuYahtbXsZjVCGM6ZN41IsIdD3dfi4709wh3nnwoX5w7d665o+LuChhd4w5Xtum6K64hnZ558eLF0e3bt9ut6cGd+Zs3b9qt6RD1qNEIjapBn00jL1++bOybwh13aSRCkCd5x1HnL1++tGsn8Im0VWPc1J+cDF0wIhXLkRJHcMiX+vTw4cPT7T65yJtzdo1GbaK8jLgyOjiUGtvSRkoPu2Jomabcbvf505UrV5rRWvVJ1Jl4PLKpPwE6xaa6Hnk+ePCgWU/b0K9fvx7duHGj3coTz/n+/XszKtmF0jJqif/FcqbXZqbg8uXL7dY0/SmdDVMZ8Js+aC/S9orz0csSmU3ASLCIk62C3MZRGAbuggpMulX0v3EAY44afZ8/f77d+uFQNCw0WHT0gM5Xd1ZNACmH3QQ5YtoITQXqFc8gTRkCWtknUrJpCgHIhQsX2q0T1FHFxnZIcEOeNOACn0Um6Js+i9DJlh5/iDx79uzM9FmOVC46O8m1S759+/aTndB5zdR8Sp9t93WTM7RMc2i3u/yJwIS2TwEv9air3dqWPx0fH59ej0Cs63rkM+TmgOCzK9gFdMA18b3SzRp9AH12lGsO/oTc9+/f79RnhPJzoxD7uzSAXBKzCBjlTKr0GDLntEKGJj1ORQCzZCOOCXdSPP8h29DgYRsanU15/fp19k58CuTuYKcIgQGdMDahkd8W2D0dhaDhzHUg8kfpDH+m04gjx6RhodOtDZLIrzQSEiEtbUDskGvkEktpP6Z8kzOHdrvPnwh2YeizekP8CRhA4eYHPUG8QYhw81UK/lLwgVLwCVEHBKxdlEaxp+xPyF2rM9oTgksGsKgPLCW7zZ1ZBIwMqeMg60CHjiPmRk5MHTQM3LEJOXvXkL0aSxofORHL0BGDKU9H06jQoals3G3TULA+RRjVANluqE1zxGku3dR1jfYRCKpRpZOhY811SrHj66s//ECqb6pNMNWX6wRq5do1lDsdeVm33SvZljobOzeuyT5GjbbNJmWaerud+hN1lccqCJAIpmCoTmv9iXy5PoEbeqLO5kbO8ZcY/PX5EzdOBK5dPpyS6iCCjfcxit3FunVvnRsqjYyz0J5ArQ7nxmympNeZmokM6QjNWQgAcTZBI06nRENEg6SGI8J+nEaOxNJ1t5mDxgvnHqPzrkF32Vq4y6SDY32KpHos2TSlpoOns0QHXagu0Lmhp666QKCjUeW++lN7Q0FHSXlzZauVa9dopDp2vjwfVRsQR0q2jfpkYT9l30Xnvo0yTbXdTusSgVbchz9wA5ljE39Cl/SF8Vrp9DYoGIyBS8mfaG+HjKwBMuTaDMqX6xP2yTp1T0G6zl0H3XQullXFGZUaEVZ3Ck26lSHaPT//urEL0sTzDo2hJkavqb6kf5HqPtqGX5bVXjN3LbFqLDvtK3m4Vi3b0EMJ5F0FHO3WcHYtH2mRUfTZNMJ58VyI+bFeI38qA7CtciATctSA7Wv13SdbTi6BTmpl6qLWtlGOWj/K1YMhts2dX8MuywTIu45covY6YqgeYrmA7XhNtrvqJ+el9S3ml+aVwrHY9qX2JR/yEOm1Ujg3ytqXXnCNtF4hG/UPOJa20exD3k2otW3UaV/dQ6603Ok2+XXJrvyj3odQW6axGV3KWkVhUNLGJa2soMZSS23lXyrooIZUb2ljJ4dgyTlFPFcNRhd91wL250jrQa2DduWXUpKNusS+XPk41tVB1LBt+Wr01GdTQbpcQ8m1dX6EtPK7vmuwT8drOxLS6ZxYfl2L/4L8Nyk75Sgdr4Fr1CIbssR6Fm0LpXoAteVDl+uUj3xrqSlTWh6ObQJ51FDSY5Svxp9iOUt1Gdvkjtf4E6Qyx34w+oYW0ncRZdai/FJ/oswxXdRBrG9a0joJXG+d+hYh71pi+aIetJ99OR2wRL1G27Aor1gvSnrug/PnwC/8WQk7GjxLsY4IDHszhB6H3M3PrKvfpTF1PUxdPh6e12MGhwLTWUzvbvo4zBJ90P60Gfan9bE/jces3sNojBkHnm3iYXA953MIXLp0afRnscwysT+ZOTLLgJHRxc+fPzcPmB6SwxkzJowM8DB+fJB8qdDGcMefPtBvzLawP5m5MdspaVOH9XvC1PVgOy2XJdrW/mTGwv40HpMIGI0xxhhjDhUHjBX4TnC3WL8nTF0PttNyWaJt7U9mLOxP4+EfvRhjjDHGmCIOGI0xxhhjTBEHjMYYY4wxpsgsAkY+ts4cv5b0o+nAtzBjGn7GXyKm5yWqNfD6A9Lnrm+WAfWmtg6pPmjZx+sxhsgH0Xeo8xFeSaVjtT5gzFBKdTAl1m+Wmjq+CfYnYwbwz8jUisAnePTZI/6nxOO1nx7SJ4H4X8uQtFNgAiaeBEP0oM9c5epZhDTpsi5Dzq2Vj89ZKY0+YZV+0kqf/WJ90092mTxDbDsXastUqoMpuc/LrVsnObcW+9O8GGLbuTCXMs1qSvrevXvNf17a3Xenuk3inePz5899F2maUeZVJ4GXN//FPutlCUY7375923yKC86fP9/8f/bsWfOfFwbD1atXm/+rjvDo0aNHzbox26CvDqa8fPmyadvxKS0PHz5sj46L/cmYmT3DeOXKlcYRgcZFENB1fZOToE4BHkvfdHJMr6/IkHcMDO7fv99s83knc5jQkanO8V/18ty5c83/sfn06VO7dhZ9movOL8dUAl4zf/rqYAo34/gRbe/UbsbtT8bM8EcvGmWkcRFfv35t186Cs5Lu1q1bTYAH3PV1PWtGMEn64+PjZuHTg0pL8Mg2d8A0EjzLYoygXnAjMZVPX3X5BHTVf2O2SakOpujmXGgmZyoBl/3JmBkGjLdv327XThoZnPXChQvtnrMwPUCg+ObNm6oHmjWFwHkaKXr9+nXzX6OMBAR8A5Q8jQHqISMjHnE2Zj3UvrJw8yWePHnSrhljxmZ2ASMBGyM5wHMjBHSaGsxBZ86dapwG+f79e7uWh/SaYvzy5Uvz35guHj9+3NxETImumyiYyiioWTalOliC+kngyMzQVPzK/mTMDANGuHPnTvOf50ZKAR3TGUwj0/AMcer44LVHjUwJHk2INyPp1NpYXL58uV07ix7axydy6KF9Yzalrw72cffu3eq0u8b+ZMxMA0acUCOACh4hTmXAt2/f2rW650w0cqlfvnGOfwlthN7ZpueqqBvctOhBfZYhz21tmygfN0h0Ygpm5QsPHjxo/tMZg8qC7zx9+rRZN2Yb9NXBWF81ExSfDWcGacwbdvuTMQn/jEyNCCtHbdKx6D2IvONq1VE363oHVlw4tnLaM9vKh/X0nV96fxbHtE/5zxnKYer1EO2vhfoVj7FNPUzTsageDYVza6iVT0TfSWWLfiO/MtsH/S6NIWXqqoNpfVWaXNqhcH4N9qf5gX6XxlzK9At/VsKOBndwI4uwaKzfE6auB9tpuSzRtvYnMxb2p/GY5ZS0McYYY4zZHw4YjTHGGGNMEQeMxhhjjDGmiANGY4wxxhhTZBI/ejHGGGOMOVTm8KMX/0p64Vi/J0xdD7bTclmibe1PZizsT+PhKWljjDHGGFPEAaMxxhhjjCnigNEYUw2fbqv5zObc4bNwxuyaQ/En0CcgzXyZRcCIUzHHX1r2hb55yvLf//633WuWxlD7kjZ+B3fX1MhHR6S6ypJ2THwLW8dqvplOEPX48ePmu7oR+WdNHrpeX0BWE7DF8ukbvn3If9P8U7n4ZjDb++7Mo036rt2XluM5PaIrncdSq7t1GVImsU9/Wkc+9Ep6zhUxn7i/C/LI+ZPyyNmuC+p12h5IRpaacg3p22JZ0yAw5hPLcPXq1aMPHz60W/thiG1LaWMMkpZ33/40Kv+MTK0IfLeTtCx8IzrCNz/3ib4nyvesp84ETDwJ1qlntfbVOdSLddmFfNEv9O1rwblRXtZL37/lWPymbkrfceD6SkP6nPz6/ntJlpRYri5K+XbJxTnbaFtq5INoE8nbRV9afQc5J38sL/9r5YvUnjOkTGKf/jRUPulLdUTEfID1Uh3mmGwQIe9cXSxBGs5DbwK761zy66vHaT1gHX3kQK5UZ0qbbiND1APXiXKuQ5SzRLSJ5OqilBb5JbPqZoRt2SzVYy3rnDMGo0tZqygZikWVcSyoWMghh5wyc6mIu2aoHmKD24c6BzU467Bt+dSACTWCavg4PzbcpQaVdH0dTp/8qX6QPXYkgAxch3RRthKUM80nRW1Hrt3ok4v1Tf281rbRPlC6dk1atlO7yc7SRbpdyy7KJPbpT0PkK9WjbfhTWmbkYF8JpYn5sS/mU5JFcDy2GWl5hHQQiWnTsrEdy8A1+uzfR19ZBOliGUq2LaVN7R2vL90qTbpdS8xzysz6GcbclEUcGhZxaD4OlzN0zNB73BZxCJolHYZOiekXPSRtzoDd37x5025Nh99++61dO0HTXufOnWv+rxq0o2/fvjXroOO5uvvy5cuje/futVs/wzmrDqrdyvP8+fOju3fvtls/Qx43b948+vvvv4/evn3bTF/VwBTX9evX2608165dO1p1Ys2Cf0b65CLvFy9etFu7Q3qP5b548eLR+/fv260fDEmbgp3Rg6ZMX79+fbTqGH+aFt0G68i5T38aKt+TJ08aXakexf5nG/7UVxdTZEPqKL4jkD/m8/3793Ytj/q2tM3I8fHjx6NVMNVunUDZBbpkW3lS1gcPHjTr4suXL+3a7timP0XfwOaroLfd2q8/TYFZBoxyWDqWCIEhx1aBcFOp5dB//vln8x/oYGRwOhLQNg0CUNnJmzzkDL///nvzPweVhfSkJS/la5YNNxs8gzQHaBSpz2rICPCi7KXne3ie78KFC+3Wz9AplDq6XIcUG25kw/cIFlnvCz4jdLKljg7fpE2gDGm70CcXXL58+UyHuCsINpAzgs7RScqQtDkUlCt4fvjwYfN/2wyVc9/+NEQ+/AOdEexQj6gTbOtZv039qaYuRqjXBIrYru+miSAvLWeE/jEGgcheW+cVbOlGFI6Pj5v+kvqFXDF4+vr1a7u2W7btT9gnF3PAvvxpCswyYKQy47SxY8Gg7NedFncIGDF13D/++OPo/PnzzTqVJGdcnJb8SVtyNCB/Oi3S4RjKWw2AWSa5u9IpQ0BGfRaM4uAvNHIsqufrlIcOkMCqCzq0NAjEN+lMFCxqVKkv+IzUBJf4Jvmr07pz585pR1CSK6UUAMwRys3y6NGj07o8JlP3p0+fPjU+Ih+iPhFkafR5U38aUhcVLCq4TG+akI38BDYuzRCk16GskJOdQAp/F/gWcsu/GLRhH0EjqD8UpSB4yigmYPSQYDjt36fmT7ti1lPSuakLnAeHxXDQNxwfiXcUmqqOd1qlTkONhUYX93UnZcaBRpE6pg6CekfDy/rU6Bq5oQHUQmMXRxlqkU+ow+jixo0b7drZ0RQCROlNeqRBptHVXb0WjeYIRk5ivimSLQazjCbEX252ybVv6FhjWwO0IVFWMSRtDnRJ4EP7SQdIm7WLgHiInGP406Z6TIO5Tf2pti7KR6QriDJjW/WDLASvDIyU/Cn6SDriGEEerqU8sJGCaEbuCSapVwSb1C3sGkmD012xK39Cj9iW4wI97MOfpsCsA8YcGDM67jp3rDgWjddQh4/XXfKwtDm5WYn2pq6o7k0JdTwlPyANDXn6rJEodaA8s1MavcgROyQaWumQRpvOjXXk1V29ltSnGN25fft2u9VNDGa5dleQWeood43sEzsapj9zsg5Jm6LRD+kEnaJzjSptkyFyjuFPQ+TTowlpIBCfHRSb+JMo1cWoJwIUlnSqlWMKgjQi2OdPQPmQnZH4LmQr6g2PYFGXOA8Zoq+l0+6qe/u4IdulP8Uy7tOfJsHK8KNSKwK/YCIty8oR2r1n0XH92mnlSM16PBe0vTJss72q9Ge2OY/tlcNmr7tqyJpt0uW2OUfrY4NcZrgeqAtDbEhdoR6syy7ko14jVyS3zbW7fApIk54nkKN0LiCnzme9q6wcq9U5Plajb+QjLZC3fBxq5EKH8Zx1qLVtlEftThc1adPyArYiLeWK2302TNlFmSKcsw9/GiIf8kSZSKu6JciL/UP9KcrBeq38Xf4nu9YQfYT1VLYc8RyR6oM0qmdAvpRtU2rLNcS2tWlTvWp7X/40NqNLWaMonJR0cclVahlaiyonFTeeF9OkeXNOzIfjOp91KoaOsahixH2xURkb5DH1epDD52ypupNrDDi2id23LV9az7WoYeM8tmtkxh/wgQh+EvONOiEt14/oeixdkIZr9RH9Ocovv415RF9OywB9clEOtSPr0pV3jmi3qNNoW9GVFmK50uNRJyw1Ok/hvFqGlElwLNp2KLuSL+o1BkPaXyMz+h5SF3P+BMiV5iPfyOXfBfnouvE6qieqH116EjEflqifnKzrQt61DLFtV1rtY8mVYd/+NCa/8Gcl7Ggw/z+yCIvG+j1h6nqYunzpg/aHAFNUetXPJizRB+1Pm3Fo/sTzjTx+Eqdz18X+NB4OGBeO9XvC1PUwBzvx7BUPsR9KJ4dNPn/+vHEnt0QftD9tzqH4Ez+uuXLlyumzgptifxqPxf3oxRizGxhp42H8+HD4UqEzpwHfxoiIMTkOwZ/0ztNtBYtmXDzCuHCs3xOmrgfbabks0bb2JzMW9qfxmETAaIwxxhhzqDhgrMB3grvF+j1h6nqwnZbLEm1rfzJjYX8aDz/DaIwxxhhjijhgNMYYY4wxRRwwGmOMMcaYIrMLGNMPqOu7nLzracqvJ+D1ApK5T07S1nxvdFtE2VL5kEP7edns0onl7bMBelJaln3UvyHyQbStvnsqoi8dgm3NOJTqYMT+ZMzE+WdkhoiQ+/wSn/BhH0v8nM8UqZWTTwvFzyptAterIf3kYUSfS5wztXoAfeqp71NWUV9a1mXIubXy4SdKI/uq7mlbdmV900/gmTxDbDsXastUqoMpHEuXdRlyrv1pXgyx7VyYS5lmM8LIHdvbt2+PVg579ObNm3bvUfNi3VU5ju7fv9/umS+6O7127drR77//frq9L1YNXLt2cictLly4cHTu3Ll2ywAj2qtOoql7/BelEZR9wugM/sKn7eD8+fPN/2fPnjX/eWEw6IW6+NWjR4+adWO2QV8djNifjJk+swgYcdbnz58363xKKUf8TmU6bV2a2qChUrp0SoHz4nYkTmXEY3HKAuK6eP369el+ri9oLFlEur1r+HyTgkYax65plSH6XSoPHz48/aQX/+kgYCqB9adPn9q1s7x79675j31zTKWDNvOnrw5G7E/GTJ9ZBIzRWXVn1wUOyugcgQ/B1q1bt04bnxw0VDrOeQrQCFC5m4zbCowICj9//twcOz4+bo4p8CNwFQSV8W5ZfPny5VQ27kIJwAQBGLKzjBGMoQ+N1lKuKBsM1e+hQH1Ab1P5lNzXr1/btZ85xADf7J9SHezD/mTM9Fjcr6Q1Ask0KqjBiSN5XSi4UwCkaYYIARN3k+kdcG76gbtP7pYVdIoHDx40/+/evdv8j9chLUEbC+tjNJgEvTGI/vDhQ7MOm+h3qRBUo694s2CMWQ/7kzHTZBYBYxxV/PjxY7s2DEb1mGKNU6ldU65dfP/+vV3Lk95pTuXueB34ML7Q4wAl0O+h8vjx4zP6mgIK6HPMuV6a+VCqgyXsT8ZMk1kEjDxIzNQnvH//vvmfkk6dply8eLG5Y2XUTsu272DXbTim2uAwLVQL+j1EeDwhPpPVVw/3xeXLl9u1s+ihfflTih7aN2ZT+upgDvuTMdNlNlPS+mV07scYbOsZE+5OQdsa9bt9+3bzf1MUvCqY0qhj7lfaXc+26BxNRWuKemoQyKbPYO5av1NGP3TSg+zUOz2eoFHrTZ7b2pQoH7ajnqrz/fbtW/M/fRxCZaE+6wdPxmyDvjpofzJmZvwzMkNFWDlhc05cVkFNe/QEtuPxlfO2R34mzW8V+J3ZXjUSp+urhqw9659mXfs5R8T0rEc4B1l0nKUk2zbgGn1EmWNZBDqKcg7R71RAzhqiXbWofukY22k90aL3sA2Fc2uolU9E26ayRTvm7G62A/pdGkPK1FUH7U9mHdDv0phLmX7hz0rY0eAObmQRFo31e8LU9WA7LZcl2tb+ZMbC/jQei/uVtDHGGGOM2S6zGGEkzaGzrpl8p33C1PVgOy2XJdrW/mTGwv40Hp6SXjjW7wlT14PttFyWaFv7kxkL+9N4eEraGGOMMcYUmcQIozHGGGPMoTKHEUZPSS8c6/eEqevBdlouS7St/cmMhf1pPDwlbYwxxhhjijhgNMYYY4wxRRwwGmOq4Vu/XZ+8XBJ8Fs6YXWN/MnPCAaNpvuHKMxRa4re6adBy+5cO5f3vf//bbnXz119/neqHRd+S3TW18gnSYstItHuNbWn0+ZY439UVnFfbGXSlRWeSg6VWh6St0UHMO00fdRBl45vB7Nt3Zx7l6bt2KW3UaU5HOr6P+jqkTFP2p1gOLbHOxLaypl5u4k8l+3K+jmkh3y6wSUxbslGfDiAe03Xn7k8l28Y6m7axi+OfkZmACItmiH5Jy3ejU9JvYs+RWj3wfVjSdukiZVvfjN2VfKBzoh05N26zXioLx+I3dUHf2uV/H6W07Ffe+h5viSE6iOXSd9z1DeD0WqkOSF9Ttj76yiOiTSRrF6W02uY/kC7ajnM5ziJdDKUkW2RImWCq/oTsaf0nvc5BbulSeabpI5v4U8m+/Nd+wfGSneP1kIu8c/TpADjedS3On6M/lWzLdqyzlC/VUQ21ZRqb0aWci6LmyhD9yhmiw+NIcXuuDK1nOH5sCHOgl3Uahxy7kE/QENKoqUEEzo92LTWopOtq6JGh61hKLq2uy//cdokaHZAm2ihuc27USboN6K3vGn3U2pZ00Sala5fSss4icvaTnmMeQ9hFmUg3VX/K1UfO0f5Ubvlcjpw9BDJ0HRMl+6Zysl3KL5W7VC/6dABdZRYl+9eyi7pXSluybaoT9q9Th4fW17HwlLQ55erVq0crZzi6e/dus61hefZH4hB8nI5gqF5D9qUpkKXw5MmTo99//70p81RhiuTNmzft1g9WDd3Rt2/f2q2j02mx3BTgy5cvj+7du9dubReuu+p0TuvL69evj1YN9Zlpuk24efNmYyOgbNTX3377rdm+cuXK0du3b5v6DC9evDj6448/mnVx/fr1Zv+ukd6jr128ePHo/fv37dYP+tIy/Yfc4ty5c4299z0dOKRMMGV/Suuj6pL2q04J9lPWHJv6U8m+qZz4U+laObmBPFP6dIAfPX/+vLGffCpljv5Usm3UCWXGBmn6JeGA0ZyBDpPGh6Dv2bNnRw8fPmyPnIBTfPjwgduhZgE6exwFB9M+Go6lg64oLwGOAuUpgTw8I5WDG4N4rBRM0EFduHCh3do+f//9dxO4KVBI69wmYCPKSt4EJDF4poN49erVaZBCOdNO8fLly40/7BqCdwLnCDpHNyl9aZH3/PnzzTqoTN+/f2/+74shZYKp+1Pk48ePRzdu3Gi3foa61FWPN/WnIfalTebGqBaCp/v37//kBzlSHeAr2A/58Kn02UaYoz+l5GxLfdWN6ZJxwGjOQENBg/3o0aOjO3futHt/8Oeff57eRbLg/DgQ0OnT4OBMChyXjBpVynt8fNzobN+jOF3k7pojBE7YTnZUg9mVftcQ1LGgQ8m+TajT1M9cEMIxYDSyi6nYdclM2Z9SGCW7fft2u3UWbqq5ORkbdEfQM8SnkTsdZe8i1YHsx3+1/11B/1z9qcu2CpRpY5b8wxcHjOYnuhpCQQeLg2ihUVKgee3atSYAmfLowC6gUSbg2fcoThc0anS4CggJ8uMoHkQbIjsjC2OATHRSBLGqQ3QoNM6Sn2WdOkXjzSMWMQghX+A/N0DxBmesxp4Ro3Tk5evXr9lRmr60BP+Mmgh1zrlpxl0ypEwpU/OnCDc0cSo2gq6ZgdnltGStfdPp6D5/YrtrRiKlpANBG/Tly5d2a79s059En23RBTMWXSOTS8ABoxlM2gjo+TN1vDhN7JgPiX13yl0QfMWAkGCQDliBUQQ7EUw+ePCg3XOWmg5+XTSaqI6HOkSH+OnTp6ZhjmXg2FAoFx0CEISgBxp94H/s8AgeST8GGgVS5w/4WW7asy8tI6V0eIKgC52WOvddMKRMXUzFnyI8g6jnvFPQfd8I3ab+VGvfdASw5E9qq2WzPko6iHQ9x7lrtulPosa2amsWy6rijMoERFg06+h3dbfVnBd/NSb4BRjH9Esw0vLLMP6vApJmH7BPaabAUD2sGuBBv+ajrEPSp+xaPuwR7SPYz7WxXxekYcmBDMhSQy6t6lqsT33yiBodkCbKzrauxblR72ynOiJtbfm6qLUt15es+F7pvFJa6VDEMgulyfl4DbsoU2TK/tSVd7qf/HL1GH3EOhnhnL76VmNfdJ3z9xycm8rTJZ+o0W+uHHP0J0i3u2yLzlNb1FBbprEZXcq5KGquDNUvjsA5WnINR0wj58d5cBbtr22s9gUy1aDGOFcOdME+0qTp+hrYPsijhlr5UjgW08pWNXaiAc018spDi66rBpd6IrrSgtJrYbvEUB3EtFEmUHqWXBk5np4zFPKuJcoTy5ArV1daiDpN5adD07Hc8Ro4r5aaMrEoDQvHNoE8akiv21eXcsFVWn+1dPkW6Tf1p5J9ARlrAheVMV10bq0OIJU/B+fNyZ/6bBvPYSH9OnDuHPiFPythR4NnKUYWYdFYvydMXQ9Tl4/HDnglxpJfGZHCFBXTUJs+k7REH7Q/bYb9aX3sT+PhgHHhWL8nTF0Pc7ATz17xIPuhdHLY5PPnzxs/+7dEH7Q/bY79aT3sT+PhH70YY6pgZIAfhsSHw5cKnTkN+L5/KGIOB/uTmRseYVw41u8JU9eD7bRclmhb+5MZC/vTeEwiYDTGGGOMOVQcMFbgO8HdYv2eMHU92E7LZYm2tT+ZsbA/jYefYTTGGGOMMUUcMBpjjDHGmCIOGI0xxhhjTJFZBYy87JS5fi05+DZtX5oaeA3Ar7/+2m79gH3KO/14uzHGGGPMEplNwEiw+Pz583brhDQgJFi8du3a0dOnT5sHSG/dutWkGfqeK67FC0ZTCBbfvn3b5H18fHz06NGjJq0xxhhjzJKZRcBIIEjQR6CmQFDEUb67d+82/69cudL81/azZ8+a/zWQXxqYwl9//dUEi7r21atXjy5dutSkPYQXrxpjjDHmcJlFwHju3LmjN2/etFtHR3/88Ue79gOCNo0Kkj7y7t27dq0Mgen79++bQDDlw4cPzf/cm+o/ffrUrhljjDHGLI9ZBIylzwlpNLEUtBFI1owCMiIZA9NIKegkmEyfr9Tzj3G/RyKNMcYYM0dm9aMX8f379+Y/08NMDW8DfuRSOxKZg1HPOFWuwJP99+/f38pH140xxhhjxmCWAePLly+b/7mp6XVgFPDJkycbB3RxdDL+GObixYsOFo0xxhgzW2YXMPKcIT80efXq1VaCMKaJye/3338/nTrWs5D8yIWRxyHwC23Qj2H4Ec3t27ebfcYYY4wxc2R2AaNem/Pbb7+1e064fPlyu/Yz/IiF4JLgT0Ghlv/v//v/2lRlbt682a79zPXr19u1o6OHDx+2a0dHr1+/bv57dNEYY4wxc2ZWASM/JCFYjEGZXqtDUKZfN+sZR6Fg7++//z59NY+W//N//s9P+5QPzyRyDigozP1wJQ1WeWYReE+jfpRjjDHGGDNXZhMw8kwgU8QEYXGEkNfgiD///LP5//Hjx+a/th88eND83wRGNAkgkQGYGmfqmuAwHUHU9Qg8t/WjHGOMMcaYsZhFwEiwmHuZNty4caNdO3mZtr7AQjCpr7Jsa0qYH7UQNJK3psZzP7zhegSS/JDGGGOMMWbu/LIKqP5p10eB4GtkEXYCQe62fsW9CUvV71Cmrgfbabks0bb2JzMW9qfxmN2PXqYKzzZidL1Oh1fpGGOMMcYsgVmMMJJmCYyh6iXeja3D1PVgOy2XJdrW/mTGwv40Hh5hNMYYY4wxRSYxwmiMMcYYc6jMYYTRP3pZONbvCVPXg+20XJZoW/uTGQv703h4StoYY4wxxhRxwGiMMcYYY4o4YDTGGGOMMUUWHTDyXIC+NW36QV9acnrjHZP//ve/261lU1N39O5NLcaYn6HdkI/kvsUv7E/GTJtFBox859kNzjB+/fXX5nOGPHjLN7L5vCJ6FASKXZ9nXBJD6s7NmzcbfbGgO7203RhzAjddBIJqV/i+fhf2J2OmzSIDRr4pTaNTapzMWf7++++j69evN+t8Cxvdffv2rdkGjvPt7KVTW3foBNGJkO6MMT/gxvPx48fNur6xnxu5tz8ZM31mFTAyyqXpCkaC/vrrr/aI2RTu7n///fdmHd2i699++63ZNj9D5yfo7NDdFL4dbsxU0AwFN2GCT6a+f/++3fqB/cmY6TObgJEA5t69e6dTG9euXWuPmG1A43zr1q0mGH/y5MnRmzdv2iOmBFP5Hsk25meYoUh948KFC2dGElPsT8ZMl1kEjBpJfPjwYfNfU6Zm+zDt/PbtW/9YqBICa01h+7lZYzbD/mTMdJlFwPj169eD+XXuWHBnf/fu3SYoPz4+bp49qpny5zw9JsDCdNIh8u7du+b/oZbfmJTz5883s0GR2rbc/mTM9JjNlHRpGsNsDqOKNPDAM0c8nP7hw4dmu4RGBLTEZ5EOiUMttzFd6NnFGPR9+fLl6MaNG+1WN/YnY6bHLALG27dvN3eqnibdHUwBvXz5st06ucP3LxXroW4SZLujM+YHPOLy7NmzZp0fwfBqLj1aVML+ZMwE+WdkakV49epVkzYu7MuxCi7PpLt161Z75PCg/LVEna0a+nbvCegwHkfHcwKZayjVnVUHdlr2tD6m+hoKeZhlskTbDimT/IYlthv2J7MOS7TtXMr0C39Wwo4Gz72tIwLPwfBrXr/6pcy6+l0aU9eD7bRclmhb+5MZC/vTeMzmGUZjjDHGGDMOswwYGV38/Plz83JXv7zbGGOMMWa3zHZK2tRh/Z4wdT3YTstliba1P5mxsD+Nh6ekjTHGGGNMkUmMMBpjjDHGHCpzGGH0lPTCsX5PmLoebKflskTb2p/MWNifxsNT0sYYY4wxpogDRmOMMcYYU8QBozGmml9//fXMt4GXCq/uMmbX2J/MnFhkwIgD8kyAlkNwyG2BvnLf7OY7sNJn6Tj/l0BXOSNpPdsX//nPf06v2Ve3o3w525BXbWNOusePH59+37evTuQgnc5Jifqseb9qzXVr2wKl03G+pV5KvyuG2BYdKS1Lzr5puSDareu8bTKkTAK7EkztC2SrqcOlsiCvjtXU3039SWlZkEusa1/SDr1umr6rDHPwJ6VjSXXWZ1uVu1bXs+WfkdmFCJcuXWrXfnyv9FCpLfvx8XGTliX9jqu+r6zvwPJ95fgdb9LrXPKZIshWQ0kPKWk9Y1mXWvmQSd+3ll26QD7ZSWmjfTjOvliOLihbtHlfnchRupb8tKb+bGIjzskhXag8wHqNbvroumbKENtCTX3LlYtt2Yr/tfJFdlUmkH113jrUyjekLpXKgi1Ud5VniU39iWO6Xgr5DLHvEB0gl+qdZJYcfWVg/1T9Cbkka1quPttyHfbFc4ZSkm1KjC7lthUVKyikxj80huoXx0kbDRxGjQSgy9Txp67nbeghQnkj1Luoo6HUypfqmGvm5MzZiHSpjOxL06Xk8qqpE5GSPmtkyFHKE2rbAspBWo6ldu3S7xC2bVsgXVq+lFy5pIOu7Vp2USahoESd/TrUyif66hKUypLqr3R98kjrO3mxiFyaSEwb2cS+NTogTax3cbumDBzvu0YftbYt2SuSk5N0KkuNbaXneL0h1JZpbGY1Jc0Qfhwyzg0N//bbb+3aCRruP3fuXPPfDIfphOvXr7dbJ7pcOcjepxemhOoVoAc+U/nHH3+0e3aDpjuuXr3a/IeLFy8evX//vt36wbdv3xobRS5cuNDYcigvX748unfvXrt1wpA6gZ/iu8gp/43pHj161OSvYzXTYjXUtAXIRjnOnz/f7jkLx168eNFu7Y4htoUnT540dQ595egqFzpYdY6nU5ivX78+WnWOZ+rzthhaJmDq782bN+3WdOgrS9QfZVgFUe3Wz2zDn54/f97YPu0Dd23fmzdvNvUO0Al+LT+rKcMU/amvrRxi26Uzm4CRiomTrYLcxrjXrl1rj5Sh4qzuFHbSIB4K6Dt2PNLl9+/fm/+HDA0IDfQ+oGFLr0XD9vfff7dbP7h8+XLzPwZfX79+bdeGQcPJdSJD6sSff/7Z/Od5Lfz31q1bTccDdHgqE8dojAkg1eBvk7QtoCP78OHDT4FlBD2mnckuGGJb4OYEfREMpEF2X7nI8+3bt6fB5sOHD5v/22ZomSgDdWSK1JSFuoxO0W2JTf2JOql+kOCNvjGyS/tS7/Bf8uamJQb3NWWYoj/VtJW1tl06swgYdRelik9FTCtDF1TqXY/8mMOFBpPGm/qoBnoK4CMKvpCLhXUFavuERpZAQHf7rNNpEMAR2CCTfJsgB10yCrNt0raAUZjatiE30jMm6ozR2/HxcWNbyVhTLjp9ll0F50ORDHFEaG5QdxXEE8ilo3/bQrbnP9eDdFR+1/aljPh1et1apuRPNW3lvmw7dWYRMBLtp3dRNUz5jnVO0IFzxybk7HFqL4fuyrSs27jMAU1f7LIh5O49vTsv+YYaORadd+fOneb/pqxbJ6AvKIijj9uqP2lbQN4ahWHRjAXlGqMzGGrbCPokOGAkp6Zc7Ceg5GaHDpA0u6i3Q8pEMB87bKZcVY4pMKQsBPHYg+O1bOJP6O7Lly/tVrd9t+FPzKjcvXv3zI2K6tUmZdg2u2or17HtkpjNlHTXNEYXqsRzvmOdCnTg0UHomGgcdKfbRXRCFo0iLZE+XWwD1eXYudNR3Lhxo93qhlEnGrp1/CHXyA6pE1z348eP7dYPkIVnmhRsR9i/rfqTawvSvOn8gM6CY/tmE9sKOua+cmm0SXZCp9jt06dPzfY2GVImjdRr4dEB6g3rU2CofUrtwab+lIPn86Bk3234E0E8wRigE+zELAFsWoZtsok/9bWVY5RnMqwqzqjUiLBq7Jp0q7ulds/Pv9aKsF+/cBLp9qEw1MToNeoZpH+R073SrPsrsV2zDT2UIO0mdaxWvngddF1zHmlWDWC7dRbyo6wluF5atpo6IdhPWs6BND+OSde1ZYIaG9W2BbquZBSc36efPmrLs45tARm79JCWS3aTrbSdlruPXZeJc7rqbA211xE1dam2LNJpF+ShfER6TsmfUmL93MS+NTogTZQ9yllTBrajvOtQ0m1knbpHmlK9S8sotJ/rrENtmcZmdClrFUVFI21c0soIVJA0XVfaQ4Cy16AKryV1GjkcS9qopLbpa3TGALlqKOlBdYs02y4zedQS6ziyiChfLEdX3adsSqPzcmD7XCPfVSe0P+6L+so1yDrG0iWHiGVj6bLRkLZAMqfXJo8p25aFY13kyhXtxsL2UDivlpoypXAsV09qqZUv1WVXXRJdZdE+lr6ACH1v4k+p36YMte9QHcS0qW90lUGQ35T9Kdc26BhLzm7b6As4bw78wp+VsKPBsxTriMCwPs9ujDF9NCfW1e/SmLoepi4f0zSaJj4UmM5imm3o4zApS/RB+9Nm2J/Wx/40HrN6D6MxZhx4iJ4fjeh5wEPg0qVL2ecrjdkU+5OZI7MMGBld/Pz55B1Uh+RwxowJIwO8UzE+SL5UaGO44z/oB9zNTrE/mbkx2ylpU4f1e8LU9WA7LZcl2tb+ZMbC/jQekwgYjTHGGGMOFQeMFfhOcLdYvydMXQ+203JZom3tT2Ys7E/j4R+9GGOMMcaYIg4YjTHGGGNMEQeMxphq+Jbsofyq05hdY38yc2KRASMOyDMBWkw96Cv3UXq+USp9dn20Pup97q87KpUzZUjabcBLf6XnUmcT02mJDTedlfbXyM+5vDtOr8aoqRMRpWVBNkFd0f7ajqVWB6B0LPrWroj5RJl4Xxz79t2ZDylXKW08luqUbR3TEsu+bYaUSVCfqJ/7Atk29fd9+xOQTufkQKZNy5Wi67GU/CnWuzn4k5B/RJ+I+cT9QufEMi+RRQaMvE2eB0hZ7t+/nzWwOYsaqxw42rVr15p3X6LT9+/f/xQQomNezHp8fNykmesXDEp6SBmSdlvQoGMPdIw90HkO0vAlCfkBy9OnT4/u3bvXHMdedFbsx2aPHj0qBvmk58tK8aP+fXUiwjHVDRZeXAzo8MOHD6f7oS9QqNUB0IC/evXqNC0yq5OL+bCwrraCTpz0tCX7Yki5SmnR9fPnz0/LhQ5ULo7ReesYy61bt47u3LnTHN82Q8oksA/1cR9sy9/37U+ALC9evDi1Ywq6f/v2bbvVzRAdlPypVO+m7k+A/OiBNpJz1EbFfFhYV7mA9krnYNN93ujsnVUhR2XbIqwqRrt2At955LuRh8pQ/a6c6qdvYaK/qEO+F0o6Qfq4PUW2oYcuhqTtolY+0qF/gV1y1079AJBT+9NvpvL92C4/Se0NfXUipSvvVA62+3RRq4OcTKSTLByL+aCb9NpdeQ9h27aFUlr+x+8Bx+20XrBdslsXuyiTUF2MZRhKrXwCHfTJJXJp9+1PffJyPsc3LVckJxPpJTfrXfVOkLZWni52UfdIR/qudjPmE9sJnRdJ09dQW6axmdUII3cs3AGwcFeTuwOLb5LnToCvwehOwawHIxKMVolz5841d2zoF7ib5g5LtuGOzGwf3clrVAIuXrzYjEakRD8AzsV/tD8dAWY/eeV4+fLl6cik6KsTEY08UDdSn03lOH/+fHEkYIgOvn371sgUuXDhQiM7cIw0QrrRNYAyMoqza4aUqy/tlStXmpEl6Rr51Qam9eL169c/2XZbDCmTYHTmzZs37dZ82Lc/4cvoUW1umo6RrocPH7Zb26HPn0r1TkzRnwB9rYLJpu1Bn3GUsNROfPz4sRmhj2Ab9i+R2QSMGEHDvhiQofASGLzU8Zh60DcduZDDfP/+vWkcpGdss7rTbgJIOazZHjRaaZ2mwa75mD8N2I0bN9qtn6HR7+pgOMZ1IqU6kXL58uVTv+UGDl/uAjlpvLsYogOuC/EG5uvXr+3aUdPQM40ocp0zeSD3rhlSrr60dJL4Ibqm88N+aaAo6EDp6HfB0PqKnaI95swu/YnPCYKmwKnHcap3V0F3nz/V1Lsp+hN+T6D75cuX03aKbZWz1E5wTlpGttm/RGYRMOqORQ6IQdLKkILDYHzSUXnNbuD5Mxor2YY7bXTOXbSZDtzV3759u906C/5VCtI2RQ0q//FJ6BqFJoDZ1vOvXE83MLQBLKyrc6WNoHPQMbUpcVRC5ILJqcOICXQ9N0aZ6EBz5d03uRGhubJrfyKYIYCRrlinHqPDXQbdff4k+uodTMmfPn361Ph+HIW/f//+6UjokHZi6cwiYOQupjQqUYK7HJhjgz8VcBDu2IR0ybRJDjUUNJxyMpauIMHUwQhEende4xt0JKRR4BbBlgT9Q4O0oXUiQmeauwNPR0Zy9WeoDigXQapGDiD+wEPHWBhJoKMYgyHl6kuL3hiF4iaOckGcYhO7nI6GIWWiTsRAhEcYCIxYnxNj+FMMXNAhs2/SI/pnH/bfRntc8qfaercP1m0rRXw8AFRmlthOMM0tWwm2ux5HmDuzmZLumsboI9dJmmEQAOJsgmkSGjh0i2MpKI+wPzYuLF1TNKYOdQyxgSLwKk01A6O9d+/ebbfOgm37nvHNNbKlOlFD2qDqV6aRXP1ZVwfANWjscyMDdHYEKA8ePGj37Jch5epLS8AS7UAnTtlSSqPO22BImTQjpIUOGVuxPif24U/oJfeMHPqOOmQhD0b80O+22+PUn2rr3T4YUvc0TR7TAjZJSdsJHudIYxO2d/WYx+isKs6o1IiwMmaTLv7CaeUIP/0yLQfn8OuoQ2WoidFr1DNI/yLVfbRN7ldjU2AbeuhiSNouauWL9blW111p0v3kja1TuF7qQ311ogRpI+QdzyXvkj7X1cGqc2u3zkJeHM+VHblSeYdSIx8MKVcpLcfS7bTsnNOljxp2UaYI5+xDPrENf0+vSZpd+BP7Sau8c/mJbZQrB9dP7cO5sQxsp2mm6k/IGWUlLedEyIv9qU05T7bKlbmG2jKNzehS1ipKThKXnEOl6WodYKmggxrUYGlJK70cjiWn03hurpEcG+SqoaSHtMHo09kQOL8WycESdZ1r0PAHNZoi2jIuXfKTPtfId9UJ7dc+8lU6lgj5xmNaYhly1OiARWlybYXkKtmN/DZtQ7hGLTXlEl1pIR7L2Y7jOZ3UQr61DCmT4Ng+/IlrSzaWeM1Uvq60+/YniP1cSU9cp6/+1uogpuuqO9HWXfVuqv5EuZU2lk/7+/RMmlyZa+DcOfALf1bCjgbPUqwjAsP6PPcy9FmRQ2Nd/S6Nqeth6vIx/aTHDA4FpqiYllr3cRixRB+0P22G/Wl97E/jMav3MBpjxoHnsnjGkGd4DoVLly5ln881ZlPsT2aOzDJgZHTx8+eTd7odksMZMyaMDPAge/pw+BKhjeGOv/ZHPMYMxf5k5sZsp6RNHdbvCVPXg+20XJZoW/uTGQv703h4StoYY4wxxhSZxAijMcYYY8yhMocRRk9JLxzr94Sp68F2Wi5LtK39yYyF/Wk8PCVtjDHGGGOKOGA0xhhjjDFFHDAaY6r59ddfD+Y1IMbsGvuTmROLDxj/+9//Nk5p6uBZCnSWg68TdDk++zl3KQ1DSQ+Chp50WvYFdtA1+zqbKN///ve/du8J+IWO1bzPFNvysmG9S438dH6frqLMrOeI+qyRp+a6QF7KlyXVQ65e84Jh0u67Mx9iWyHfk16jXeKicit9XLpssg3WKdO+221kq6lL0JU2lrOmHdzEnwTpdE4kypIeS4l+x1JjI9KVZFSeymsO/lRqJ0q2jectPdZYdMCIwR89etRumRJqrLrASZ4/f95unQUnuXfvXvPQLp9rnLPT9OkhwmeuKDPL/fv3m0Zl19BI0/BxTV5ez9cTusBmr169Ok177dq100YQWemsOHZ8fNy8BL8E6bHt1atXm21kID/yJY/37993BnlRZhbWU12xTVmQhTSlT6YNsRF8+PDh9NosKgN01Ws6ccqGjffFENuCOir5Hl8PgY8fP54pr/Ki3JxD5x2P37p16+jOnTvNudtmaJlgn+32kLpUSoteqUfSKfWq1B5s4k8CWV68eHF6zRTtzx2LpO1YyUa1+kr9Zg7+1NVOlGyLPuJ5vIy9z26zZlXIUdmlCHwsnI+Mlz4avnSG6nflVJ0fh2c/xyP6MH6ENOyfEtvUA6waoHbtBD5WT11bl1r5SBd1yzVzcpImtRXpJGMqf+n6ubzIJ5Y3l0ak9YFrx+vl6lUNnFOyEXBdbFOidP0u/Q5h27YF0pE+tSOk+8hD+aTH2F5H97sok9hGu10rn6ipSyKXlu0ob7odQR+pzikvi8iliZTkZX+uXuRIfYPzUpvlKF2fcpBvrn7W2L+PXdQ90nW1E5zTZdtc+framxxD6+tYzGqEkcieuxsWIvtSJM8o15s3b9otsysYzVg5T7t1AnZi/5KJn7jiLpYROo3w7ArqPMQRsosXLzajESnfvn1r7qojFy5cOP2Wa5QfX1k1cu3Wz7x8+bIZxYqQz/Xr19uto6Nz584110MXKexHHhGn4IDRJPKXbzMysC0YxcE2NaMiOSgjozi7ZohtgXKtOq5m1ISyxVH9aFtA/tu3bzfr6bHXr1//ZNttMbRMMNd2+8qVK0dv37497ZPQeVd7sKk/cQ3aWPQon4np8CfqRc1MTzqSr/rB9dcB2SjH+fPn2z1nmbI/dbUTJdtGf+I4dijNjsyd2QSMOIimXnAkhu+7oMNhus3sni9fvvzUCbHN/kOARpnGeR8QdKXXIghkGiTl8uXLzf8YfH39+rVdO4EGjgaSxrAEnRnXieCDsVNQHfj+/XvzP8INRfTH2Lkhg8qEbxO40uGpwd8UGnbyJbiirEODUfRIWXfNENuiP2yGj6k9ZDtXNuk69VFBB0qHuAuGlAnm3G4TmFB3FXTgM10639Sf+P406JES/EtTvdibfSyALEN8ibT379/vlL0E12Z6thQwTdGfoNRO1NiW/RxfOrMIGBXZP3z4sPmPsdLKIHJ3FsbsCkZDaGiojzQaUwEfUfCFXCysx2eIaNjVSNLYyc+2DTqik5Ac8l18lA4GmeTbyMRxRmG2gRp28j8+Pm50EAPWWtY5Z1d8+vSp0VEc5aCTz43clEYQKRMd6BTayqW02/gS7PJZPW4OCBalK9bxL3QYAxn8Dnnu3r3b7umHkbaukdE+eK6v9twp+RPUtBMl29KO6satZmR3rswiYGRkhBHGGqjwsZPkYVWMyLrZPgzzp47FNvs1gqVl6OjOnOCuE3bZEDICkd6dl3xDAaEaM8j9uIFGklGKdASyBAFLnGZWubumsiQHC9ciwOlCDfK26w8dLNfOjdqMzVDbpsTpzEicjk7Z5XQ0DCnT3Ntt6iojf/gSdRyGBA5D/SlSCrKRRzbo86dNRnjJW/Zi0Qwg5eLYvtnEn9J2ota2uknvGsVcArOZkq41gkZ8tNAxYXwZ2mwXprNS27DN/hiwsOBwSyXe2e8KdQwxKGVa8saNG+1WN9z94wddnUtJ/lwjS1AXA0waVzqHPj2oY3nw4EGzTaCjYDvC/l3Vn3Wfz9olQ2yrab305iQd+WDECdt12aQUTG6DIWWae7vNSHnUMwEG9TzHpv6EXnLPiHf5NvlAyZ8U1HXl0UeaN6N0QD3l2L4ZUve6UDsxxLbxsYIlMouAkUaNirfkEaq5gmPSAKrBwUZsr9vwzBXKve6zP0NgWuTZs2fNOgEBIzF9gRR3/ECnnINGtZQPZaLRjBDwMSIkmPZilKgEQStT3/iy9ERnEn1bZdpFJ0MdpcMYYiNN/+7arlBrW2QhaNCrPQC9pqPHTOt3TUf2BZPbYp36OkeYUaFsIvdjQIHON/EnjsUpU+pB14g9x/TMYxf4BfJEu8S6tU2m6E8paTsxxLbYrK8dnDWru4FRqRXhVfsz/biwr4+VI230eoa5U6vfVad9RrepztiOx0kfWTUCzX7+TxFkq6GkB+oS+0iT1sdV49SmWg/yqEVysEQ7RPliOXJ+omMsfTbjlRO5NOxXHrH82q99qjslP1Q+LLFMOWLZWLpslKbjWEpfveacqdlWRNm7bNwF+dW0n12U8k4ZUibBsVJ96aNWPq4t2Vi66hKU0kIsZ8mnNvUniO1PlCNtl/psHGXOnTdUB0Iy6zxBflPzp7RMHEuJ+UTbxf0slHsdOHcO/MKflbCjwejHOiJwd0wkP8Zw95xYV79LY+p6mLp8jDhomvhQYASHqcJNn0laog/anzbD/rQ+9qfxmM0zjMaY8eDXjzwQr0cPDoFLly5ln680ZlPsT2aOzDJgZHTx8+fPO30ViDHmLIwM8DxUfJB8qdDGcMe/j2etzGFifzJzY7ZT0qYO6/eEqevBdlouS7St/cmMhf1pPCYRMBpjjDHGHCoOGCvwneBusX5PmLoebKflskTb2p/MWNifxsM/ejHGGGOMMUUcMBpjjDHGmCIOGI0x1fAN1UP5Vacxu8b+ZObEYgNGXrfDcwFa+DSQ6QdddX2CkZfN5hyffVHXLDWfluq6Fud25RPtustGqEu2SJRzHzJFauSLyEapPqPsfTYjD94dF1+NwTk1ZaZj1HVycku+mrzoYJUXS6nDrU2LTEoDvC+ulH5XxDrVd+1SWto7HUv1vS8fErVlium07Fo+5InXK8mXpk3pq+MplG0b/pR7tVyqyxJ95UqJaWO/Gutcmsb+tBD+GZldiZD7vM8hUqvfrs9SCT6HxLH0k1Z8Qir9/BOfiyp9Iql0Lbbj56ZYly05L9oVWbo+TZXCtWro04OgzOlnt0hfOqfEtuUT+lRYLi3HSnaKoPe0vF11IoVzdR3JH/PChpKP/X02jdcjb/LroiZtVxmwcV/ZaijJF0EHKrs+V9ZFKa22+Q+kk77R/7o+FNl2mTiW1i/OVb0YSq18m9SlqEfWS3U8hfTpcfLnvL46l7tWSpStj1K5UkgruWVPyZLaiuMx73R7XUo2itTWPSil1Tb/YUx/GpvRpdyFojBiyVkPiaH6pcJ3NdLsTx1eTiTYrm0UctdinxogiM6b2pTt2vJtUw+Qlhs4J7e/hm3LB+pMumSq7VTIh+vlyNWJlNRuNKi6tmSMkF+sA5E0L9WPXPqatH16RM4+PfdRa9tUttK1S2lZl36BdLLRJj4U2XaZxvCnIXUplYNzo47TvGIdT4n2SEE3XcdEKktaVvKo1VtfuSI5ubmW0qd5cSy1dZf9h7DtugeltKyrjBD1kNqd7Vr5IuucMwazmpJmuFdDvwwR54bigW9M8xUY0pndkr65//Xr10f37t1rt4azanSOvn371m79yB97p99dPX/+fPO5qTFIy4181M90/5jgB6tGr9ERvsA0lsB3nj9/3uzv8iPx8uXLjWya2g0dXbx4sVn/+PHj0apzbdYFemR/jlxecO7cueZ/pC8t5eZa79+/P21X0qkovvf74sWLdmt3UH/g6tWrzX9AR8iW0peW6T/kFpQXv6Js+/ShIWUaw5+G1KUoB3qkf+HzfqJUx1M29acoCz69ClLarRMePXrU2DT6exd95YrQLlOPIhcuXDj93F/MC/Cb27dvt1sn2J/mzWwCRhoPnGwV5DbGunbtWnvkZ6jwpKOzpBOoeZ7EbAec7MqVK+3WcAgeeK5HpB14hKCCoGgKIMuNGzfarfFBb2/fvj368uXLqc+wLV+4fPny6X46CfyrCxpMOoZtQX4PHz5s1pEv7WjYZn8NNParu/+f8siRpuWzbEB9QxfUvZs3bzb7BHpKO8ldQGecdjTonM/HpfSlRV46LqHyfv/+vfkf2aUPDSlTyhj+VFOXCMJqAoJYx1O24U96bg6fjuD31GUWII0CohI15cIXIPanX79+bdfOonY71aX9ad7MImDUCIgcEIOVKrcMSvrj4+PmjqsUeJjtgI5xsninNpQ3b940DqoRH9k5lyfBaXqHNxa5u+kx+fTpU6M7jRbgE3SGuruXj/Bfncs+bqzw5W02qOTVNSKSkqalsyVYVN1inbqX62CX2n5MyYciY/hTTV2ifcJf8C3apxzbruM5sBlyMCjCDZ/6SPk1ICvH79692+7ppqZc5M1oJv2p2mfW05ss6Jtpsj/Nk1kEjNzFlEZAStAZMHKQuxsw2yVtJHQXrKU2IKHh0oLtCHRSuCOmkZsCBBi7nj7bBnFaJYUOrnZUb13oJD58+HCmQWXqJ+082GZ/X/1hO45Gl6hJu8mNzqYwgpGOvHS1e31p6fQZNRHSbzrVumsfGlKmyBj+NKQuAaOEkKu7aR3fJQyK0EZ2jfRxXDaoaY+7yiUUqLIo3zt37jT/I2PfQC/Rn6bAbKaka6YxSuSeSzHbJW0kYuPC0jVF0wUNHKNADx48aPecwOsPhjTuu4Znkmru4veJpn7Shj83GiC6nrnq6+Br4drpCA6PL6S+zTb7S/VHIyo1QV5XWjpappBSxggcdc1oLwL43LRsX1r0HAMIbpbp9GIAtg8fGlKmyL79aUhdEl3BbK6Op2zLn0RfYI3toaY9HhKkU4fwoVRvU7iBXqI/TYJVxRmVGhFWHV+TTr9agpXBfvqFUg7SxPMOjaEmRq9d+mI/x3Pwy7FV49Fu1VG61v37J6+4wPYR9ke7c7zGvtvUQ8rQvHPsQj7sEW3CNeIvASNddgV0zpKjVCciafk4T7ZFRtmU/X31iLSpPF3yldJyDLkkB/vTtKSpKV+JWttSdl0fO5XOK6VVeymQX/oFzovbtT4U2UWZRG3eJWrzKNWPErFcIr0maVS3IpzXdQ3OGVLfUluncJ0un8+RK1cOrtnlp5wf61fE/pSntkxjM7qUtYrCIKSNS65Syrhaair/kkEHNaR6SxsDtuNx0kdKjURK6Vq6Tq4xwlHjeVpSWXKQroaSbJQxvR5l3kYd25V80idLtE/cz1KCBjTXyKd56LpqcGk0tZ4uUW6Qbfs6E5UxXVS2qIO+tMC69qcyAXkMbfxTyLuWKHO0YyyX6EoLUe9R/k18KMI5tQwp0z79KcoVl1xdivWEJeq0to6LTfwJYpo0n1ROlaWLUrkg6oBF6Ur5crwL8rM//QznzIFf+LMSdjR4lmIdERjy5rmrfT0rMlfW1e/SmLoepi4fUy48A3lI/sYUFdNRmz4Os0QftD9thv1pfexP4zGr9zAaY8aB57J4RkfPex0Cly5dOv0RgDHbxP5k5sgsA0ZGFz9/Pnl/3CE5nDFjwsgA7y6MD4cvFdoY7vjHfHDfLBv7k5kbs52SNnVYvydMXQ+203JZom3tT2Ys7E/j4SlpY4wxxhhTZBIjjMYYY4wxh8ocRhg9Jb1wrN8Tpq4H22m5LNG29iczFvan8fCUtDHGGGOMKeKA0RhjjDHGFHHAaIyphg/sH8prQIzZNfYnMydmETDiVMzxl5Z9wXsfdc3//ve/7V6zNIbal7TU031RIx8dkeoqS9ox/e9//zs9VlNWGn1eNqx3qQ05ny9bKC3rOaK8Ne9XrbkuRJ9lQe4I8qQdGi8YJu2+O/Oop5prky6ng5JtKKuOaemyyTaI7Xeq+y5ks30wVOcgHeb0hr6VX4lN/EnIZ1K5Y5lY+hiig5hvzp7xuPQzB38qpY11OGebofqeLf+MTK0I8VuO6Xcac9/l3CX65uem38TcBxMw8SRYp57V2lfndH07toZdyBf9Qt9JFfgQ2/IlZC99H5bz4/Eh5yNn1A3r5BeRfJSvj6E2Sq8V0bdgc20IZdtG20L+NUQ9Sb9dlHRQsg3/tV9wvEbvkdoykbd0yLWjXF1I/tpr5Kg9d4jOQWXoqnccq6kzm/hTRPU31WmpzqcM0QHXk1xKG+sOx7rqEumn6k+ltOhSZZLfpbYZou8ctWUam9GlrFWUDMWCQceEioUcXY3GlJhLRdw1Q/VAw1ZrX+oDDYYanHXYtnxpg5Y27sgbGzn2dzXmuWNDzme/rgtpg0w5us4twTl9NuK6fR1v6fqUsbYedFFr22gfqLl2Tgcl26RtJ9vr6L6mTLKzZEGOuN0F8pCu5hpd1J5LulqdS/5UhyJnixzRHqJksy5IT91OZUKGLhlz1OogJxPpotxxPUdX3kPYhW1LadP2Q+29IN0QfeeoLdPYzPoZxtwUYG5YOE7BxOkphtPj9EEcXk+nwfumyNaZdjHzB7u/efOm3ZoOv/32W7t2gqa9zp071/xniuj69evNOrB/1ehlp21evnx5dO/evXbrhCHns//bt2/t1g9Z5CePHj1q8pf/1E7H1fDkyZPmE6Lkuw6U8cWLF+3W7pAurl692vyHixcvHr1//77dqqdkm/TTbK9fv/7Jttvi06dP7dpZkK8LpvZ2JU/KUJ1Tl1bBQfNNZOpT7H/oH+hnOFf1uGsKdFN/Aq5H+vPnz7d7foA/IWOuf0wZogN8GJkiFy5cOLUnMj1//rwpe1d/OUV/6kuba0s5Loboe+7MMmCUw759+7bdcwIOy7FVIHy0ugM4NSDf6xQfPnw4Wt0xNOvXrl1r/mubBgGo7ORNHnIQOp0uaORIT1ryUr5m2RDY8AzSHKBRpD4rYKCuxs5G+79//978j9Ah0DFEhpy/uiM/o6fYCeJr+Czgt/gPDbAa8U35448/mnzp6Gkzhgajly9f/qmT3AV0xtKDQOd8b3goQ2xDp3jlypV2a7t8/fq1XatDQcbt27eb/7tmiM6ps7TxX758aeoTOmZb9Ul9DPWc49T5mzdvNvtSNvUnZKEfSwMZ4BjXZwHqfMmXhugAX4DoQ9HGHJdu6C/pj1Om6E9DfQ/7PXz4sFkfqu+5M8uAkQqHgXBKQWPDfjkpdwA4dOycgA5EjkklkeEjOCL5kzatSCnkz10V6XBy5d11h2WWQe6udMpwM0R9HgNGYPFNGlMW+RS6o+PDZ+WH+B7HGYXZBup4yf/4+LgJRtM2oYZ1zpk6lIlOcQp1GFkItsaqo30wWkq9lHzUK27ANFpGX4P80iXr1PldBA8MUHTpSfUd8DtulO7evdvu2Qzy1g2dfJl19bm6Nv8VQHXdoM3Vn+jXNbAEu9T3FJn1lHRuKlBD4lRkyN2hdRHvKKjo5BPvhkqVXB2iRheH3l2beUGjERtO6h2dButTg7ocR/iAzo87a6G6rSnrPoaer7tw3ejR2XahDojGWfpl6ep8aqEz59pD2oR9wY1mbGuANiQ3StNHrW12OR0N6ShaCWRRG4r8Ypf+tKnO4xRyytAgvNZm+ITaGRb1N5yfG6TgRkllzPnTUB1oMIVF5925c6f5n0IbyYjsGAwpV21abNI1siuivpfIrAPGHHQIsXNa5+4ZxyIYKHVqOeJ1cyOXZjlwsxLtTV1R3ZsS6kRSPyAoo1EUBFF0OvGOWeQa2SHnR9ThPXjwoNmm09UzUBH2x86JZVs+VRsU7xPZR4EC0NneuHGj3aqn1jaMju1y+ldTmCm6IRibITrXVGpMCyoLvv/x48dmPZLrfzbxp9QnGDUHZOsKZMgHcv60Sb1jpJNy58oo4rN++2RIuWrTYqOaEXDpe5GsKs6o1IrAL5hIy7JyjnbvWXRcv3bi10usx3NB2yvDNtuv2l+aaZvz2F4FAdnrrpyk2SZdbptztD42yGWG64G6MMSG1BXqwbrsQj7qNXJFtE1djtckP9Ln4Jw0nyHnC/LgnNR/2Rd9p1YXQ22EfLn07COvHJzTdayW2vIgh/Rcq4ecDmpsQ/77qK9cQ/pDBs6T/dnPNrJEJH/tNXLUnjtE55Ql6izKnpYt5zMid6zGZjkks66bwnVS/aasU+9I01d/ZPfIVP2pL226Tfqczmv0naO2TGMzupQ1iqJiki4uMm5EhtaCUUENk86LadK8OSfmw3Gdz7oaBi2qNHFfnyPtE+Qx9XpQw52zpepOV0Oxid23LV9az7XETijWc/lKDtLlGvmu87Vf+5CR7ZJ+lA9LTr+RWh2k6TiWItm0pNfmnJJuaiDfWqLdoiyxXJCWLdVtn23JryYg6YJ8a4k6Ri6hdjWVI5ZtXYacW6tziGVJ5Y59Q2qPyKb+FNExyRhlYKm1cY0OWEr5pr6Ug/ym6E+QSxttEhfZd119p3DuHPiFPythR4NnKUYWYdFYvydMXQ9Tl4/pJ00THwpMUTEN1fVryVqW6IP2p82wP62P/Wk8HDAuHOv3hKnrYQ524tkrHmQ/lE4Om3z+/Ln3ucw+luiD9qfNsT+th/1pPBb3oxdjzG5gZID3zcWHw5cKnTkN+KadmzFd2J/M3PAI48Kxfk+Yuh5sp+WyRNvan8xY2J/GYxIBozHGGGPMoeKAsQLfCe4W6/eEqevBdlouS7St/cmMhf1pPPwMozHGGGOMKeKA0RhjjDHGFHHAaIyp5tdffz2IX3WW4Bu8ue/2GjOUQ/Gn3OcQzfyYXcCYfkBdFZFGfMqOR8MgmacmJy+RjTplW0S54/6lQ3mpU32k9fF///tfe2S31MhHPYuypUTb1pQVX3v8+PGZV2NQJ7o6A/aTd01nUUqLTiUnS02wVpIrB+VX/iK9rmzLN3h5HUqNzoYSfbGvnehL26WDtFy7prZMaX3tK/+2GKJzge3xn5QoP/mWwDbRn6Jd+upWyXe76m2JWh3EdFpiHeuSi+/Fs29fNhVDbZvKHSGvGn+q0fds+WdkhoigTw/Fzy7xCR/2scTP/EyRMeTkerWQNvfZptJnruZCrR66Ps/VBZ+T2ga7kC9+fgw5o6yskxcoz9JnrUifHten3XKfOaPOSD7OK9WhvrRDdVySK0dX2qgT/rMdQU7psER6XhfoQGVXu9ZFX9ouHSit2qBcuWqoPWdImaKs2HwducQu5BPyF50n0GVNfYDUn1K7kHc8Hunz3bjN/74y1eqAY6lMnMsCfXJxfq1PlugrjxhiW8nLovJEuvwJ2D9E3znWOWcMRpeyVlFqQLoqHMflbFMF+Vn2KeeQiiinkdMDzhO358oQPQD1LNdwRNBL2oCuy7blS+sYcuIjIpWbhjUej1DOLr9DhvSY6lGENLl61JeWcvTZIUdOrhykyeWvDkZ6TLehpJdIrW1JF3WEPbrKXpM2pwP2pXYuBSddbLtM6fWl73juELYtX0S+omBEpHrtguulduHceH4ujSj5bk29TanVQS4PZNT+mjalK+8h7NK2lKcrDftTm6T6TbdrqS3T2MxiSpqh5OfPnzfrfEopxx9//HE6tJ9OE5aGohl+Vrp4HsPPnBe3IwxN547FIXmI6+L169en+/umHvbJ1atXj1ZOfnT37t1mW3pjfyTqKQ7RS5f8T/W1RKiLv//+e1PmqRGnjbEjcuIjIv0cGekvXrzYbp3l5cuXR/fu3Wu3+vn48WNTjyLUE/an9KV99uzZ0aNHjxodl/x4HajHXOv9+/en9VnXQB+rzuG0HuOzqw7jjF7xi1XHsJUpKOURfQ17IFvKkLQpX758+UmPlOnDhw/t1vYYImeuPsK5c+ea/7tgHT3Svr9586bd+gF1iT6KOsR6iZw/MWXLt6UF5aZu5ep8yXdr6m1kiA7SPDgX/9H+mjaFMr548aLd2h2b+MgQhup77swiYPz06VO7dnR0/vz5di0PFYXOEaOtAuKmM8KgXfA8ko5zHucAzs+H0uO2nJdGA2fm2OoOpjmmwC92yjjT6q6r3foBjbZkozPsa2D2CfJTNspDZ41+IshK54L80g3Ogm5wxqivpYOuKC91jY5iSsG/oK6W6r+gw0ptLTh24cKFdqsf6nfaYLLN/pS+tA8ePGh0jB/Fhnkb8Bwi8BwZ18Af8XnBp9vevn3b2BZy+uGcb9++tVvrQx6pndA5MqQMSZtCh02Z1KFCLijZBpvIiXz379/facc7VD78m7qS4/Lly00dou2kH6Ht7yLnT5wX+zaV+/v3783/Eqnv1tRbsYmNuKm7ceNGu/UzuTYFPVHWXbNJuYYyRN9zZ3G/ktYIpBxSjlfTmSu4U0VThxKhIaNyKI3ugAn8UnAY7roURAk6QdBIXu46Y4G+CIAoz507d9q9P0BW3Umz4PyUE9QR4TBpmZeI6hbl5cYBne2q810XRkOwBfVVDVoKNwFdI/djIx3jR9Q16l4MdjaB+koAoFEI1rlGzJ+AUDd2Xdf9+vVruzZ90COB2LVr1059GD3E0a0pQH2MN99jI9vHEauI6in/1fbt4wayy3dr6u2mMFJ4+/btdussfW3K1NrJTdmHvqfALALGeOeVm9aqgRELRifUSLIMHa3ou9tLnUCNyNzoagSERm+1cIdFWdmvjmiKo227hI6EBqNmRGAMFNSndZRtRozT6aRNYOond510egqGpKWOEewwegDRl0sjOrWkwQD5ErQQdKtup7JuC9q4dOSFQDRXriFpc2hknEU3ydu0v1hXztJI3jYZIh/BD8GA6hs3LnFUKYX0uRH1LrihU70G1bPSlDxpcr7bVW8J4iQ/C3pe10YERaTJ9XFdcu2TTX1kCF36XiKzCBjVGUPXMwg4Qwk6oNhQsrC9TdYNEOcWWKYNoQJvjSzSCdG49tlkiezymatN6KpjTMH2+cHQRvbKlSs/Tf2wzf6UIWkFnQFEX07zqIE2JXcDSnujUQLpjbpNpx4fj9kmClZjR4Of5ab8hqTtQ4/v7IJ15FSboXN3yRD5NFKvhRsX6g/rXeRueiDnT/ghAY3gxpP6Vuobcr5bqrcEcLEMHFu3LvEcpmbIUmralF2zTR8pse92YnRWFWdUhohAWhZ+7RRhe9XoNev8Koo02l45dbO9uttotnOsDNykWQU6Z7b1CyvW47byBF1PMnEdpU+vqf1pPiXZNoX8h6IySM4IOuKYdEVays5/yiPYpzRTYKgeqAOqQzVQ1iHpU3YtH2lTv0mvSZpcXeS89FzBOciSQl2Q/UkT60ZKbVrqY5ccKV1yRbgmOlCZYznlA5JL26l+2Jfzk0iq5y6QWddXu9JFTdqSDnQOadZhF2VC10or0u1adiFfhHNKdbpU9zg3LZfql+B81b0cqZyUgzxq621kHR10pemSSyBXn1/2USMfrFMuZOO8HOxPZV9H3zlqyzQ2o0s5VFEYjXPikjoW2/F4n7PEtFSwuE2joPVYWVjX/uj8MX3aoHCOKpSWoRVrKFxjCDl9pMQ00gnlKJV9bJCphtQ+sRyqG6RJ0+X0NATyqKFWvtQHsJlQ45kuXTYjfdpQQrQ3C9eNyEfSc9nO7culjfnX1qkuuVTuqIuopzT/VE9sR7r0ksK5tciGLFGf0baiKy106SCWNz1nCJxfS02ZYpq4pG17LZxbS418KRyL9SXVd4muehPrW85f2RfTxCXKkqZhu48hOsAm7I/UyAWcF8u2DuRbS225+K90LKncqX1jXuvoO4Xz5sAv/FkJOxrM/48swqKxfk+Yuh6mLh+PHfDDiDGfS5oa/AI9/mimiyX6oP1pMw7Rn5geZrp6ncdHIvan8XDAuHCs3xOmroc52Ilnr3iY30HjSYfPM2o8s9THEn3Q/rQ5h+ZP2OTz58/F5zJrsD+Nx+Jeq2OM2Q2MDPBapfgg+SHCr0sZHaoJFo3p4pD8ieCYgGjTYNGMi0cYF471e8LU9WA7LZcl2tb+ZMbC/jQeHmE0xhhjjDFFJjHCaIwxxhhzqMxhhNFT0gvH+j1h6nqwnZbLEm1rfzJjYX8aD09JG2OMMcaYIg4YjTHGGGNMEQeMxhhjjDGmyKwCRl6Wy1y/lhx8DLwvTRe8Kyqey8LXHCJs6xjvYzPGGGOMWTqzCRgJFp8/f95unUDQFiFYvHbt2tHTp0+bB0hv3brVpKl5MSrn8hb6FD79JQgW37592+R9fHx89OjRo0YuY4wxxpglM4uAkWCOoI9ATYGgiKN8d+/ebf5fuXKl+a/tZ8+eNf9LvHz58ujVq1en19Ci78T+9ddfTbCoa7P/0qVLTRB76F++MMYYY8yymUXAeO7cuaM3b960W0dHf/zxR7v2A4I2jRCSPvLu3bt2rRsCv99//70ZkWRqOuXDhw/N/9ynjT59+tSuGWOMMcYsj1kEjKXvT2o0sRS0EUiWRgEZPYyQPn1GsRR0Ekymz1fq2ce43yORxhhjjJkjs/rRi/j+/Xvzn+lhTRlvAh+Az8Ezimkw2QWjnnGqXCOi7L9//34ThPrD68YYY4yZI7MMGHneEHJT0+tAcKdnFtMfvnQFkznitHn8MczFixcdLBpjjDFmtswuYOQHMDxvyA9UdhGEkSeBI7+0Xgedpx/DMK19+/btZp8xxhhjzByZXcCo1+b89ttv7Z4TLl++3K79DL9mJhDMvWex67nChw8fnp4HN2/ebP7nuH79ert2cp54/fp189+ji8YYY4yZM7MKGPkhCcFiDMr0wxSCMgI80DOOQsHe33//fTr1rIXz9MMURi8jBJh37txp1hUU5gLMNFjlmUXgGUj9KMcYY4wxZq7MJmAkqOM9iARhcYTw/fv3bYofzxt+/Pix+a/tBw8eNP+7UBDI6KV+5ELweOPGjdMf1TCiyY9akAH0om+Cw3QEUdcjgN3Gj3KMMcYYY8bkl38YZhsRgr4+EQgW06+8iHTEkUCOwE/UFg85IgSH8UcsQl97gfTaEWRmVDKdOt83Nfo9BKauB9tpuSzRtvYnMxb2p/GYRcA4RwgYt/Ur7k1ww3nC1PVgOy2XJdrW/mTGwv40HrN8rc4UYVoboxMoAq/SMcYYY4xZArMYYSTNEhhD1Uu8G1uHqevBdlouS7St/cmMhf1pPDzCaIwxxhhjikxihNEYY4wx5lCZwwijf/SycKzfE6auB9tpuSzRtvYnMxb2p/HwlLQxxhhjjCnigNEYY4wxxhRxwGiMMcYYY4osOmDkuQB9a9qU4f2R6CsufEsb+HpOeowl/fb2kqB8fXVH797UYoz5mdi25L7FnwPf46taxpjpsMiAUQGOqYNGnM8Y8tCtFj57eO/eveY43+aOx/iG9lK/kz2k7ty8efNUJ3xTXC9tN8acQOBH+xLbjT7wwUePHrVbxpipsMiAkUCGBqqmcTInpN+8fvHixdHt27ebdf0Xr1+/Pg0ml0Zt3aET/Pvvv9utoybgNsachcDv8ePHzfq//vWv5saqb+T+yZMnTTpjzLSYVcDIFKmmNrgL/euvv9ojZhNoyCPoFl1rf3o8BpOHStQJwePvv/8+iW+HGzMVaEcgzkTwydT379+3Wz/DNPSbN2/aLWPMlJhNwEgAw6iWpjauXbvWHjHbhinoGzdutFtn0TNIaRB5qNDBeSTbmJ/59u3bT75x4cKFMyPzEUYeNRppjJkeswgYNZL48OHD5j/Bijvp3VEaQVzydPQ6MBqiKWw/N2vMeuRGI40x02IWAePXr19Pf7Frdks6HZ2SBpOMsOkxARaNQB4a7969a/4favmNSTl//nwzGxTpast5bpHnHdWOPH/+/Ojt27fNujFmGsxmSrprGsNsl5cvXx7dvXu33TpLLpjUCJuWQ52q9hS9MWfRaGG8ifry5Uv2cZe0HeFHL7du3WrWjTHTYBYBIyNa3Kn2/brObA539ukvpkUpmDx0qJt0cg4cjfkBr+d69uxZs84NJ+2LHi0yxsyLWQSMdMKvXr06M2WRTnVEuKNVGs7xC2Dr4FnR0ussSsHkUijVHb2AmDToSnWRBfwraWPOouAQH+GHirHdjv5kjJk+v/wz8pg/DcY6IjA1ynMvSw9gNmVd/S6NqevBdlouS7St/cmMhf1pPGbzDKMxxhhjjBmHWQaMjC4ytcHLkv3ybmOMMcaY3TLbKWlTh/V7wtT1YDstlyXa1v5kxsL+NB6TCBiNMcYYYw4VB4wV+E5wt1i/J0xdD7bTclmibe1PZizsT+PhH70YY4wxxpgiDhiNMcYYY0wRB4zGmGp4kfkhvGg5971jY7aN/cnMiUUGjPpahxZ/SaAe9JX7BCOf9ZI+S59opAFcwicc+8oJY9UzfSGj5ppRPmwooj270qTQ6D9+/Pj084e1daLmWuTNviEdC+mH2qgLpZM+3717d2Z7X6xr2y49pOUSsknJ3tuitkyStSbtNhmi8/QLT1F/MR/W+1jXn4B2Vmlzr5Zbx7411xV9aWVL6XMO/gSlcpFXrn1S2xWXGvvPkUUGjDdv3mweIGXhU3eXLl1qj5gu1MDkwNH0WS90+v79+2wjhaO9ffu23ZonJT2kjFHP0DH24JrYo3RNGjI+qam02FAdyMePH09lj3ldvXq1OZ5CA8iXlXS8tk5A37Xo/O7du9cc4xpsl9jERl0NOekidOLIme7fJUNsi44oj9LyGctccJCTn+tgu30wpExT9yf48OHDqYwsqsMxHxbWS0HDJv7EuQSapDs+Pm7eRxwZat8h/lSbdm7+1Fcu2lI+jZuCjQiGZXeWW7duHd25c6dNsTBWBRyVbYuw6iDbtRNWFaW5xsqx2j2HxVD9rpzqn6dPn7ZbJ6wa72YR6JJ0EfZxXu78KbANPUS2Xc9q5UuvgV1ycuZsRDrZEXkjHOsqby6vmjohStfivLTs5BPL2EWfjdLrYrMos2Afx5AjPYdjpWvUsG3bAmWPdTDdhlK52E6vN4RtlymVfWryAelSOUVaZyV/DtKRPsJ1WUQujUhtmbvOOvrjerV1vZS2VO9K+q2lS68paflrrl0qF/tTm6TlY7vLbiVqyzQ2sxphjEO/3BHk7sDSb0truP/cuXPNfzMc7qCuX7/ebp3ocuUYzd2b4G754cOH7dbyGaOeaQQpjgJevHixGY1I+fbtW2OjyIULFxpbguQVL168OLp9+3a7dZaXL182I4CRmjohStdi9JE78gh+zv5NiddFLkZi/vjjj3bPCbQhlOP8+fPtnrNwDHl3zRDbAiM1GlniXHQW62RfufbBkDJN3Z+ANg6d50aiqPv4nJD8ukZkm/7ESPMqOGu3xmeu/rQuadv2+vXrn2y7JGYTMNIgatoKR6odcqfirO4sfjKsqQd9xwZAuvz+/Xvzn0brzZs3zfqhso96Roe0unttt04gCPz777/brR9cvny5+c+0jPj69Wu7dhZ1Sl2y05lxnUhfnegivdaXL19+ui7b7N8W1M9Ub4AsTDGmwUoEPVLWXTPEtkDgS6BN8EIgE/2vplz7YGiZIlPzJ0Dn9D9Pnz5t9B59C1swTSxygZ7Yhj8RmCHDlB4BmrM/bQsC0itXrrRby2MWAaNGEjWChTPlOoAcNKbpqILZHjSasaE8VKZWz/ARRh54to2OhYX13DNE+7wrHuMOnGCKjp42Az0IngWrtVkpABgTgheChhi8DCnXVJliu60gjn7o+Pi48SfVC+oYgZB8Tf1THOHaJgRlCl4Z9VQfOSZL8KdNoEwEpLuy+RSYRcDIyAgjjENxMLMdaPy4YxNydqZNaDQZ7VVDSaPJPkZ1dBesJXZqS2Jf9YwRiPTuvOQb6lQ0Kg+5h7FL09FdlOpEifRaTBOlnQfb7N92/dF0PPmTN4GW8taMBeUao/Mdalv86+7du2eCF+SeUrmGlklM1Z8iBAWMKsYRQPkaC8cYIa1lXX/C/lwLufvA/qoXLNtsj6dU72AT267L0qejYTZT0kOHklVJlxzt7wtGpWKDRCNJQ8Add2wkWdjPXS933DFgYVniM477rGe6RgywmLq9ceNGu9UNd/90LKmcTP3RiGr0JEeukS3ViS5y12L6JvVtttm/7foTr5vmTdAFdDIc2zdDbUvnTKcInEtwounAqZRrnfo6F38SuYBOwdODBw/aPWfZlj+JmjSwbX+KzN2ftsE6N95zYxYBI0ag4tXeEeGwNJ7RIegwzXrQ8DGCIRjZYMro0BmjnhGMP3v2rFknAONVD30NP3f8kHvOlAfwsWcJOiTKGVmnTuSuRcNOB6pAAR9nexcBA3kPfS7u06dP1R33pgyxLTKhT5H+aGIqDCnTXPwJkJXgI60XyMsUMf1VV53Zlj8BAVGtzFNgqv60KeSf3gwvktXdwKjUiqCf6ccl94qDVYfwU7qutIcAZa9h1cCd0detW7faIyes7hhPj60csd37M6vGYOPXJuwC5K6hpAfVLdJsu55xbi3x2sgionyxHCWZON4HtseuKV11QvvTelC6FvlzPHedlFobpW1GqV5K5qhPIL9N6zP51lJjW6F0LF0y5so1RC9dcF4tNWWKaeIyRX9i4ViEOsj+tN3Msak/KQ1LLp+h9k3L1uVPUEobmZs/9ZVL9tWSK9e6dRXIcw78wp+VsKPB6Mc6IhDNcwc2xnD3nFhXv0tj6nqYunyMnDCCdUj+xugNU4Wb/rJyiT5of9oM+9P62J/GYzbPMBpjxoNfP/JDBE0dHwKXLl06/aGMMdvE/mTmyCwDRkYXP3/+PJnXCRhzCDAy8Oeff555kHyp0MZwx7/4Z5LMaNifzNyY7ZS0qcP6PWHqerCdlssSbWt/MmNhfxoPT0kbY4wxxpgikxhhNMYYY4w5VOYwwugp6YVj/Z4wdT3YTstliba1P5mxsD+Nh6ekjTHGGGNMEQeMxhhjjDGmiANGY0w1v/7668G8BsSYXWN/MnNikQEjDsgzAVpMPeir65vdfJ2gy/H5libn8n8JlPQgxqxnNfIBNpN8XR2TytHXcWF7Xjasd6nJ5n2yxHRxiXUlyslSA+lqdCBISwedI9UBLxiO2/uixl5C6Vhyeujy19Qeu/bZIWXivbr7lE1wrSF1Cb1yDmUT1C3JXfN+4HX9CaJOowwpyNSXl+q+lk3qXZcO5uBPpbTxWFcfCDX6njX/jMwuRIjf1+QbjyyHSq1+u75jKtApx3LfLiW9ziWfKYJsNfTpIbLNerYL+Tiub6LqW6k5ZNv0+6iR9Fupyk/ncJ2ub6mmcnJOWo+G6G6IDoTO6fr2bU4HOTnXgXxrqLUXkE46U9roeypPTn72y1b67vBQas8ZUibYxIcitfINrUvSV5oWuaV/5VliU3+K9TjWhQjpasoV6wj5lGQv1bs+HZB+qv5USpv6yKb6zlFbprEZXcptK0oOJzB2zriHwlD94tBdFZ79XQ6fNh5TY5t6gG3Xs23LB6k9kC89h31qENMyCfJI7c55sby5NCLNFxmiHKx3XbtEjQ6EGnl1CpGSDjhWe40uam1bYy9B2ZFZpNvAualNKF8sZ7pdyy7KRLq0DOtSK5+oqUvI16WrdF/p+jlfQS8sIpdGsD/qVDaMcFz2L5Ur1bfyivlHyK+r3tXoYKr+VErL/9hupNtQq+8uass0NrOaktY0AAvD97lh//jpIYaV+Xwg3+00ZptMvZ5pKu/q1avNf7h48eLR+/fv262T6b/r168fnT9/vt2T5+XLl0f37t1rt05giolzxblz55rPdeamfdLPgb148eLo9u3b7dbR0aNHj5rvzHZNF28K+b5586bdOkufDjiGvLumxl6RmzdvNnUOOJe28bfffmu2S2ALdK1pzNevXx+tOrifbLQNhpbpyZMnTZlo36cI8qEr9IeMsb5G/bF/FUS1Wz+zqT+x/9u3b+3Wj2tL34CsDx8+bLe6SeuM8uL6OUr1rkYHU/SnvrRXrlw5evv27Wm8gfxpW1+r77kzm4CRiomTrYLcxmGuXbvWHslDhcWxjdklU61ndCipXBcuXGi+Xwt0RB8+fKgKMujMODeCD8YgS53F9+/fm/9dqANUerbxaRbQzeC24HkinhPLUaODy5cvN2XdNX32SqHDunXrVqMvOquugDgHedIBKjDbVUe3TpmoBwRlyDalZ8GoK+jsy5cvp30Q21FGAgrkZn+JTf0Ju8c6LZ8StElD6kME37t///6Z4C/SV+/6dDBFf+pLSyBJ8KubGeyXBsfr6ntuzCJgVGSvhg1jpQZOwYA4NukwsjG7YK71jBGmMUZEGdGKoyux4UWXBAt3795t92xGbuQgMkQHaac8FdBXGrjUQKfPwujuNgP0TVBdoJ0/Pj5uZJuK3j99+tT4uOoLshJYxdEybjwU8BJcqN/aNvgJQRftDYv6Qup56QapBoLAGp/oqne1OpiqP5WgTMAoq9hU33NjFgHj169fi79MKsHdAMyxgs4d3W1qGdqpzYmp1TNGK9I7efkRdqGxl100Wk/HU9vJkZY7c6Fyd01liXQ6OoVgQXJvWn/o/Ag6dP7z589Py70NHWyTkr1yMKpBYB2Dq1q5KS9BgQJ0yr6Leju0TBGCHwLavhHrMYlTyBFsguyUtZah/kRQpoVrEbwC9QB7ql6jf/ZRX/r8qSb4qa136+hgmwype31pKd+ff/7ZlAl9A3qAkr6XyGympLumMfqIIxhmv+huUwsOt1SmVs80qhYDAabTbty48ZNdaPiBxi43PZtrZLnLjp0BHTudXkkPjGSRV5+uyAc2rT8a/dVCp0onxvpQHeyakr1yEOzS0QHnUjam1/vQaKJsgE7RNyNo22ZomXL03YDsC02lpoF1HG2KlOr4tvwJCGaoCw8ePGi2Y51mIQ9uCvCFkj8p6JPNuhhS7/pk3yVD6l5fWsoXy0LwiB4g6pMl6nuJzCJgZEQCZ11nhIpzqNRjVl6zfKZYz2i4nj171qwTKDDCtk7QTpnSToEOijtpwagDI3oleNi/b7qZaWIa5Kmgqch92HWIvZAJfQpGuLtGvCIKwBQg0EnSthIQ7YJ16yDy0WFPxZ+Qg5sN/VgImHK9c+dOu/UDdFoq57b8CVmQAfttoid0jTxR3ljOSG2969LBVP2plJYfwLAtPn782NSFg2QVFY9KrQj8dJ+0cdHP+SNpulVFaI8cJuighlWjc0ZvK4doj5zAdjxOejEHnSNXDSU9rALCZh9ptl1m8qihVj6hfen+yPFx9+tCgOOrRr7d+oHOY4nl1/5UJ+xLSfWY8+mUoToQHEvrtejSAefsy7bQZa9cuZSOJZWx5K/RbixsD4XzaqkpE4vSsHBsE8ijhvS6fXUp6jXWVe1jyflKBH1v4k+SoasuR7hOqf5G28RFZcvpIKaLecf9XTogv5I8NZB/LbF8sQy5cnWlhXisZN8+fXdBvnPgF/6shB0N5v3XEYFhfe7Axpg+mhPr6ndpTF0PU5ePEQdGEg7J3xglYapw3cdhxBJ90P60Gfan9bE/jcdsnmE0xowHP5LggXhNZR4Cly5dOv0xkzHbxP5k5sgsA0ZGFz9//rzTVxcYY87CyADPFzJSsHRoY7jjn8ozdGZ52J/M3JjtlLSpw/o9Yep6sJ2WyxJta38yY2F/Go9JBIzGGGOMMYeKA8YKfCe4W6zfE6auB9tpuSzRtvYnMxb2p/Hwj16MMcYYY0wRB4zGGGOMMaaIA0ZjTDV8I/VQftVpzK6xP5k5sfiAkU+2LfVD4LuAZym6PsHIy2Zzjs8+zotL16el5kJJD4KGPpZ5Xw0/uq29ZpSPT15F8Asd6ysrYGfeHadXY5DfkPOBdDonJeqz5nVZNdfts1HUZazbvC8ul37XDLGtkP9Fn6u1Lelqbbcu65QJmfbZbvfpCfrq0lB/2JU/DW2P+8qVg3QlGZWn8pqDP9HmKC1L2l5CWi4hncc2ZJH8MzK7FEGfVKr5hNJSqdVv12epBJ884lj6WSQ+IZV+Rgl9k9+U2JYeIlEX+nTUutSei0yqz+i9dB7y6RNfSiu7IK/WVebSZ/lIH48rP9kemUrnA+nT+iOkv5p6sy0bIW/cpgykEZStS94hxGuUGGJbkPypDmpty3m582vYVZlAMuu8daiVb1t1aag/cH48PvR8IH1aPzlHeQjyUn3IUSpXSq2+yDOWB1ifsj9F3+8iVy6uIV2gf11zCLVlGpvRpdylojAclWAdAy6FofrFIboaAvanDp82TttqFLbNNvUAaWOuBqnUMJeolS+9BvU7JydpUjuQTo1iKr98JUcuL9LG9Lk0kZI+c/WqhlKe0Gcjzo1tQ7oNXfodwrZtC6Qjfep/UGNbzifvPh12sYsyCcmb2mIItfKJTesS8kYds7+rTueODTkfuuRN6wPbpXzWbce6rg+Ug3zJJ5WHY+vUt0itbdNylK5NulQXKblycV4qD7rp019KbZnGZlZT0nGoneHi0rQV0xlv3rxpt8yuSN/c//r166N79+61W8sl/Qas9HDu3Lnm/y7QFMnVq1eb/3Dx4sWj9+/ft1s/+PbtW/M1pMiFCxdOP82Vk5+8crx8+fInm5IP38IVlJvr5aZ98FN8FznlvzHdo0ePmvx1rHY6ro8+G125cuXo7du3p+3Iixcvmk+2RSgj+3fNENsC39FfdX7N59bQWZy+rbEt5z98+LDd2g1DywRTbbf76tIQf9ilP0ku0dceb7sdQzbKcf78+XbPWabsT3w5Dl3m6CrXx48fj1Y3Nu3WCdiG/UtkNgEjRqDir4LcxpGuXbvWHvkZOhyeDTH7B4ekIz40aKBWd6A/NdjbhCCQACFCEJj7mP/ly5eb/zH4+vr1a7v2M3RYXQEEx7hOBB+MjafK/f379+Z/hM+fAT6J/9LA3rx5s9lHQ6wycWx1B98EkGrwt0lqIzoTrqeOgnKm9kOPlHXXDLEtwQGB7pcvX07bQ7a7Au3UtvsKyoaUCebUbqd1aYg/7NKfUoa2x5u0Y9TLDx8+/BSERqboT8CNIrrkJoy2IPpSqVz4YKorttm/RGYRMGoEQI0eBkkrg1BHE+8szH7AsXDIQ9Q9d6jp6NSY4CMKvjQKwXquY8G/kH9XEMzQualesE6nga/SECOTfJtGGd9mFGbbdNmITgK6Ol3QCM4U+PTpU6MjlQVb08nnRm5S2041KJtbuz2mv5f8KbJOe7xJufiBSe25U/InUNBHO3R8fNy0lZJxSLmWziwCRkZGGGGsgQofO8nnz583Dsa62S3p9AedlezA0jUCMnf21QkzApHenZd8g+CLu2aNQsGdO3ea/4JGsW9UIAcBC3fxQo1rzVRWXwcWRx+3VX9yNiJ/RmvoJNAR7POXuZGhtk2J05kiZ1vaRmZnpFOuyb5dlHtImebUbufq0ib+ALvwp6Ht8SbtGHnLXiyaAaRcHNs3m/gT+mTUltHdvnIxzS1bCbbTR0CWwmympLuGklOYalEnycKdN8ZXh2B2ByMct2/fbrfOBiwscVpsKagxHHIXvy66RmygmPq4ceNGu9UNd8n4QSonwVnf3XOukeU8GmBB40ojqjv1CNfNPdODLAQ6TNGlsH9b9afLRgRTUV6CRzqHMRhiW03rpR1VOkKas23UJws2Y4R1F1PUQ8o0l3a7qy4N8Ydd+lNkSHu8aTuW5s0oHVBP4w3LvhhS97ogWO8rF9P9aWzC9mIfy1opYVRqRFgZp0kXf+G0cqbeXzXBquHZ6Nd2c2eoidFr1HOE/RzPwa/CpqznbepBUP+oX5F0u5Za+ZBJ10DnNeeRJmeb9FzyxtdSuF5aLvmkKPkj+0mrvNP8OCZd15YJNrUR58ZrsZ3qifO76nwtteXh+pKtTw/IGWUlLeeI9Fzyztm2Roc5dlGmCOds0p7UXkdsWpeG+APnpPls058AXdfqr1SuLvr0JVundY5rce4m1Np23bqHjF1ly5ULPctWnLdOva2VbWxGl3KIEUkbFxmpBBVmk4Zn7tTqVw2WllRnbMfjaUOAnmvsMRbIXENJD5RRZdd6uqyrA86tJV472iHKF8uRyqRGL126/IT0uUY+5hMbWO2P+6L/5q6jYyyxTDli2Vg2sVFMkysjx7s6j1rIu5YoT9RDLJeIPqkyDbVtX8ffBXnWMqRMgmNdMtdQKx/Xlmwsm9SlLn9I2Yc/IW+UrYu+ckUdQElfEcmc2pb8puZPaZk41kVXubAn+3N2rYFz58Av/FkJOxo8F7COCAzr89zLGMPdc2Jd/S6Nqeth6vIxpa1p4kOB6SymCmsfh+liiT5of9oM+9P62J/GY1bvYTTGjAPPwvFAvJ51OgQuXbqUfb7SmE2xP5k5MsuAkdHFz58/N+9POySHM2ZMGBnghyHpjy2WCG0Md/y5Hx0Ysw3sT2ZuzHZK2tRh/Z4wdT3YTstliba1P5mxsD+Nh6ekjTHGGGNMkUmMMBpjjDHGHCpzGGH0lPTCsX5PmLoebKflskTb2p/MWNifxsNT0sYYY4wxpogDRmOMMcYYU8QBozGmml9//fVgXgNizK6xP5k5sdiAkfcz8lyAlv/973/tEVMCXf33v/9tt35AwyZd5t59SYPAsZqGgQZSebHkGky+hJDLCzvGc3dlV/LO6aELlR+590GtfH1+0KXnHKTjZcPxXWq150e7pXKnNu2jpv5EkLErbTwWy8ELhmvy3jYlWVP60nbZJtaJWttvQm2ZYjotu5YPeeL1SvKlaVOi/DVyk2ZdfxKSKcq9iX3xTdr6En32zJVh7v5Uar9Ax9P2dXH8MzK7EqH0PchDola/Xd8xBXTJcVC6CN8T1Tl8g7TvG7Dxe5v6pmek9F1O9us7p/qeag216Up6yCEZ9vV91KHylfygpOcU8pHeRe35+larvr9K/VBe6bEam8br5epPBB2pPupaIr0W6aK+SF+jmz76yiNKsqb0pe2yDfUnlpHjymcI2y4Tx9L6xbks61Ar35C6lKaNeuyrSykcS8vbZbMSOgf9wSb2VdtSSr9uvQPSDylbF+k1u6ite1BKq23+A+mi7TiX4yzocB1Ksk2J0aXchaIwWuqMh8pQ/eLQaSMtRxExTzUyEfLocpzULnLGND0ypI1L6rjpdolUxj5yekhR2Wuu38eu5Ovzg5yeU8inK03N+WnnGvPj/LRjTRvkSG39EekxriW98V+dBKTbENOvS61tS7Km1KRlO7VNqj+2a+WLbLtMOR9C9nV9q0a+IXUplYNzKYugTH11SZD/Jv4kuL7sJ/k2sS/ykmeX3FBjz1IZuuw/hNry1MgqSmlZZxE5+5XqTg21ZRqbWU1JM8ytYWGGfnNTo/DkyZPms4GkM5sTp0yYrlg1Qu3W0dHHjx+PVg1Mu3UCdmJ/jvRj+8r73Llzzf8SpF05ajN1AK9fvz5aOfUZ+fYJ9YzrIxN1rW8qZ99syw9evnx5dO/evXZrOExHXb9+vd06sfWqgW2mfb58+fLT9A/2/PDhQ7t1liH1R9NDV69ebf7DxYsXj96/f9+sX7ly5ejt27en7ciLFy+ab/xGkJv9u6ZP1siQtCmp/s6fP9/U310wRM7UhzmXdmSXvj2kLkU5qK/4VawrNXVJbOpPwHWom9gvsq59abvevHnTbuXZpN6JOfpTqf06NGYTMNJ44GSrILcx1rVr19ojP4Ojko7OnM5yyLNoJo+ei6FRjNDhp4062+yvAWdd3b1Vdwx8fxUZFAQ9fPiw+b9vaCyQg3KqTrI9pbq2LT+gwbxw4UK7NRx0Ezs22fr79+9NQ4ze1GjDkIa4VH++ffv2U2dJOahDQAfBzY+CasqZ5nP58uVG/l3TJ2tkSNo+uLHjxmIXbCInct24caPd2g81bRGBVS4Aq6lLYlN/wj+4oUqDwxw19qVd4FnKPrZR7+boT6X269CYRcCouzYFBxgs57RCBiX98fHx0aNHjw7ybmCb0Dgp+KBRlE02hcas6068C0Y0WbBrDDT2yadPn5o6KNmpc3Q2+7h7rmUOfkC9Qm/cANLRshBAxjv6EuvUnxTqNNy8ebP5n2Op7QejKDWBx77Bj27fvt1u7YeausQoHO0gvk9dTampS5vCDEttne+zr9rPOLq2D9wfz5NZBIxfv3796VdXteAIBBeHeDewCwg+0Cc2AYbuU+dnm/0aldSSjnDV3tlGyIfGkoabxplAYyqNT22QMwZj+gGdK3fxQvbS1B/2pBNm0eMOdHKb1h9GBdLRjNiWkP+ff/7Z1GmuDWM9VtAna2RI2hI105CbsK6cBDGk0Q3PPhjaFjFKCKrL+6pLXEczLCyaacPHOBZJ7ZvzJ4JkbiS17/nz56f5p2yr3u2DbfpTX/t1SMxmSnqd6ZbIIRp3V8SGnGd3UtuwzX6NSmqhMRVq3Ibc2epuOI6c4cyM9u0bTa2kweouRxa2wTp+sGmHgE5ogAVBK3bLBQSMXmuUZtP6o2PRRjxCoKlOpvWiDHT4dJZj0CdrZEjaLhilGnqzNpR15eQZv7t377Zbu2edtiitu0Pq0ib+lPoEMwdAWxRHEnP2zfmTRky1MNrPjSXrKduod/tim/40pP1aPKuKMSo1IqycoUm36kjaPSe/oOMXYH2QJp53aAw1MXot6Uu2iKwamFNbcC7bJUi7apjarRPSbfJBloiurWtpm/99bFsPQDljWbnGvn4lVyNfpMsPcnpOwTapfUTN+WmdIb1sKNAbaWrKVFN/BPnpmK4hOJZup3WXa/WVr49a25ZkTalJS5qc7JwX9Y99SDuEXZRJ1OZdojaPIXUpEssFbMdrst3VDnJe1zU4b0h9k05jG0je69qXc0vtdyz30HoHc/SnmvZLaTh3HWrLNDajS1mrKAxE2rikRgMZTosqwaGCDmpI9ZYGQlq6nJ39peMCe8T8tERbcu14DNmEnFlLrYOStoaSHiR7lCfKmquPtXB+DbXypelyflDScwQd5+zadb5sFDupaLe4P/p11/UjffUnZ6N4TnqNeCxXRo5HedeBvGvpknVoubpsIz9Nl/T8PjinliFlwo7s35Qa+aJcccnVpVhPWXJ1IuZXage34U9Cx5R2U/tShq42RcRypvn2tSmcO0d/6mq/oKZu9MF5c+AX/qyEHQ2elVhHBIb1ef4iDsObn1lXv0tj6nqYunxMcfGM5iH5G1NUTEdt+jjMEn3Q/rQZ9qf1sT+Nx6zew2iMGQd+mMIzUemD9Uvm0qVLpz9uMGab2J/MHJllwMjo4ufPn7f6ehdjTBlGBniYPz4cvlRoY7jjP8gH281esD+ZuTHbKWlTh/V7wtT1YDstlyXa1v5kxsL+NB6TCBiNMcYYYw4VB4wV+E5wt1i/J0xdD7bTclmibe1PZizsT+PhH70YY4wxxpgiDhiNMcYYY0wRB4zGmGr4Pu2h/KrTmF1jfzJzYhYBI07FHH9p2Re8xkfX5OPtZpkMtS9pqaf7okY+OiLVVZYcvEC4tjEnHe+Oi6/GqD2f74BLjpzc5NMna0pXXl0gJ+dwrRTpSp0374uL2/si6qHv2n1pu2wjPcQlp5NDYYjOxZC61AV5RH/q85EcXdeKZWLpY4gOYr76vr+IfXV85Z39aSH8MzK1IsRP86Sf7Sl9hmkX6PNHm37iaB9MwMSTYJ16VmtfnRM/qTWUXcgX/YLPXrFE9BmxGv/hXH0yTdSej7+STn6LntK8UtlKDLWRPt1VSquyxLaF9Rrd9EG+NSCf6pB01kVf2i7boItYRiAfdDqE2jKNxS50DuvWpZTUn3RtnZPzkRxd1xriT0N0wPUkl9Kq7nBNrctHI6RP6+M6lOSL2J+2z+hS1ipKFZAlNdC+oUIgR6nRmApzqYi7ZqgeaBhq7Ut9oLFUg7MO25Yv9REatlwnQh59jTi+15Wm5nyuG6+d5kce6/h0nw5A7UYpf2RDP7l0HOu7Rh+1tiVd7GhK165Jy3Zqm7R8bPfZL0dtmcZiFzrftC4J8kl1znksIpcmpetayF+SMaVWBzmZSCe502uSb0pX3kPI5ZujtlxQk5btQ/UnMetnGHNTgHFYWMQh4zilzHA6Q/9xW6TT4H1flInp02F6s1yw+5s3b9qt6RCnjZle4atIfI5sHV6+fHl07969dms4TEfx3Vxx7ty55ktNmvZ59OhR89mwnD9vCt+bXzX0Tf74ZnoN/BrZzp8/3+45C8devHjRbu0OtRlXr15t/sPFixeP3r9/3279YEjalPRLG69fv97ItnNmqB43rUsi5099PpJSutYQfxqig2/fvjUyRS5cuHD6ub9Yt7j2Kphtt35gf5o3swwY5bBv375t95xAYMixVSB8tLpDOHUYPr8kPnz4cFqRr1271vzXNg0C4IzkTR5yEDrcLnhegfSkJS/la5YNNxs8gzRl8AF8YhPoEOgY1gW/iB2bGtnv3783HSL+ygLbvOEib/zyy5cvTf7IwbaeD+M47cFvv/3WbOe4fPnyT53kLqAzTu2Ezvl8XMqQtH3QKV65cqXdOiyG6HEbdUnk/KnkIymla3FsiD8N0QG+APH5yq9fv7ZrJ9B35vpmYX+aN7MMGKlwOMStW7faPScVlf03b95strlDoNLiQBFGWeSYVJKHDx826xEckfxJm1akFPJ//vx5kw4nV97IY5ZL7q50ijD6SV2mftKQT414h46sjODcvXu33bMZnz59asqtkVWuxU2gRji40asddU3bkSVAmegUp16Hp8A269KmlK61S38ibwZEGMGkLWFhXX0uqO/kugyydPWD9qd5Musp6dxUIMGbKjLk7tC6iHcUmqqOd0OlSk460mt0Mb3zMsuC0ejYcFLvuEFhfYpo2miMhpqOlrt4IRmYdkvhBk4+p9EKLXFkY1007UfesheL/BZZx7jZ40YztjVAG5L7VeaQtCUOeToaNtXjNutSrY8MvVafPw3VgQJCFp13586d5n+E6zKgM1Y/aH/aDbMOGHNQSVWhWdaJ9nEsggHuIIcQr5sbuTTLQSN3WqgrqntTJI48DGVow5nCCETsOLiJo4PrkoljEDsnlqE+pemvNEhGnjTv4+Pj5hjpObZv1E5FWZn+vHHjRrv1gyFpSzA6dvv27Xbr8Biix23WpZw/1frIOvW25E+b1CVGOmnzuvrYTdqcTbE/7YbFBIxyFu6+NF3IHdQ6z0OljlsCp9DUuEZAuOY2RkOM2RbUR4LadRpxzuGZqXV58ODB6Yg/MEWm54VT6ITiM8ebIN8kT8E0WW5EpAtNRe6j82Ma79mzZ806bQij1l1B8pC0OTiHwGUf5ZoytXrcRl0SOX8a4iNDqPGndeoSI5SQm+UDgq9cPvanmbO60xiVGhFWjtqki8uq82uP/oCfxcc0q0rQ7F9V0DPnxTRp3pwT8+G4zmf9VfsaAy2rO7vmGnEf6aYC8ph6PWDPLluq7sjmEY5tYvdty5fWU/lChHNjmly5AH/AB1K6zpf/xGtGn4r7UznZ7qNWByLK2ZW/5Et1QH453Q2BfGuR/KksuXJ1pYU+23Juja67IM8pM0S+ITrfpC6JLn/q8hHtz9XD9Frr+BPU6ICllK+OseTKB+Rnf/oZ8pwDv/BnJexocKcysgiLxvo9Yep6mLp8jFTwzFbXtNcSYZSEqcJ1fi0ZWaIP2p82w/60Pvan8XDAuHCs3xOmroc52IkpF6bJDqWTwyafP3/eeJppiT5of9oc+9N62J/GY3E/ejHG7AZGBngeKj4cvlTozGnAF/9MkhkN+5OZGx5hXDjW7wlT14PttFyWaFv7kxkL+9N4eITRGGOMMcYUmcQIozHGGGPMoTKHEUZPSS8c6/eEqevBdlouS7St/cmMhf1pPDwlbYwxxhhjijhgNMYYY4wxRRwwGmOq+fXXXw/mNSApuX3GbMIh+5OZH7MLGP/6669mvl+LKiLfyp2y49EwSOapyclXB6JO4/dSo9xx/9KhvDXfA0/rI98U3Qc18lHPomwp0e41DTppHj9+fOZdauRRcy560bVSueMxllpyeaWkdZsllTceUx1/9+5dsx19lXfmsW+XRHn72olS2iH63lednTI5PZWgDnGO6gvEtpJ2oY9d+RNIvpq80nair96JnA4g5qVjOX/aByUfSelLq2MsaZmH6HvW/DMyQ0TQ9xz5L/imI/tY0u87To0x5OR6tZA2953PqO+5UquHru+5dsE3RLfBLuSL33NFziirvjkrsHGpLLlvpeob613fjRXyUdV7rqW80mOpXDlqdUCeqcykj+dwnPxycH5aNtIO9Ye+8gjkUt7SSxeltNrmP0R9A8e0XaPvHOucs09q5Rvq79JXmhb/UD1SniV25U/AtuRjv+pJF/F6yNUne5cOgGND/Gkd+uQTJR9J6UtbKtdQfeeoLdPYjC5lraJUkbsqHMflQFMF+Vn2KeeQiqiGLjoGjtDlKHNiiB6AepZrECPohQZiG2xbvrSOISc+ImIDCel2hHJ2+R3n9XUCXDdeO+bH+fEYIEeNXofqADgn7k+vncLx9Bq18ola26a+l7u2KKVlPZYr6puyc650kG7XUlumsRgqX19dAvTYpat0X+n60R4pyNB1TJTsKxkjHGN/jrQeqz50pVf+XfUlypWD43167qOk20hajtK1+9J2lUv6iJT03UVtmcZmFlPSDA8/f/68Wefbmzn++OOP06H9dJqwNBTNcL7SxfMYcua8uB3REHR6LE5LQFwXr1+/Pt0/ZBpk11y9evVo1Rke3b17t9mW3tgfiXqKQ/DSJf9TfS0R6uLvv//elHlqxGku7Iic+Ii4cuXK0du3b0+nzl68eHHmeOTly5dH9+7da7eGw3TU9evX262jo3PnzjXflEWuL1++/OSfyP7hw4d2a32iDoBpPOprbCdoV7Cf9JCC3OgmcuPGjWZ6eptoWjj62sWLF4/ev3/fbv2gL21J35R91aGd+idt0apj/ElX5mfwd3SF/qgztPUi6o/9q0Cs3fqZXfrTx48fmzY8Qp1nf470O9YqB3nmKOlgXX/aBdv0p1K5hup77swiYPz06VO7dnR0/vz5di0PxqdzpFKvAuLGmFTuLh4+fHh6nPM4B6ggN2/ePLOtjg0nwUE5trqTaI4p8IudLhUn13DQSUq2R48edTrXGCA/ZaM8z549a/QTQVY6c+SXbhRc42BRX0sHXVFe6hqNyZSCf0FdzdV/GkfqpgJeOqGuoIFjFy5caLeGQ32KfqvrfP/+velACFzVaEMaQG4LGnGCPXH58uXGfsiHHuLNjyANxyME23wHeJt8+/btJzuh89x1+tKW9A2kQ+fYHVIfNz9DnURnarvRMdvR52kb0Sn7S+zSn5Av9WO22V8Dfnj//v2f8oA+HazrT7tgm/5UKtem+p4bi/uVtEYg5ZAyZk1nruBOlSc3ioBD4SRKozsxAr8UGgbu4KhskQcPHjT/NZK37dGKTUBfBECU586dO+3eHyCr7rZYcCLKCegF/dABpWVeIqpblJcbB3S2q2BnXd68edPYgvqqACGCrYGbozHAP+igrl27dlqnqEdxBGVbMLJx+/btduuH/fiv+trVTqR23Uent0u4WdUNawzWTR4GLfAhDQhQZ6i3cbRMbT0+RWAxpYGAWug/u2Ya+nSwiT9NmSHlWjqzCBjj3dS6Q71E/IyEqVNiGTptqjv0LlInUEWbG7FTzaHRWy3ciVFW9qvjPzSHYsSODrivjoyFgnrVUTozgv8Y3MfppW1CJ8NdvJAMutnSSC2LbtrofDVio2WTOkVQxMhAySfpLMcaGaCNS4PQr1+/Zkdp+tL26RtdonNuJuSzc+rAp0LXTQ0+RVuATXZByb5Mpaa2ZJv9ff7ENr/aHkLpxm4p/pQSy1XS9xKZRcCozhhyzyBA390cBowdE0vXndS6rBsgzi2wTBsBBd4KPuj0pzbVvi+6nv0Zm7SO8VhB3EfwyMhejq6GsxZGL2PnSVBNp5er94zMaNRTIzZaqF/rwnNjGtEvMVZDr+enYueDn8UpdNGXtqRvjSZK9+iUY/GxH/MzmkpNg4Oukflc3Ra79Kfc4xJss7/kT2qrVbdyDNUBLMGfcqhcJX0vklXFGZUhIpCWJf3FEturTqZZ16+WtL0KNJvtVUVvtnOsnK1Jswp0zmzrl06sx23lCbqeZOI6Sp9eU/vTfEqybQr5D0VlkJwRdMQx6Yq0lJ3/lEewT2mmwFA9UAdUh2qgrEPSp+xaPtJGv2E7XpPtaL8I58VzI5yHLCVUnwTp07ohPxpSpiE6qNFvrhzIme4v6SpHrW3JV3qWProopS3pW8fSbf4PoSTbFBgqX01dwubR7lwj10am+k/ZtT8ho7Zr6ippU3m65KvVAeTKwbX6ytdHrW235U8pqfxD9Z2jtkxjM7qUQxWFQTgnLtFZgO14vNQYpvlRaeI2xtd6rCisa390rpg+rTicI2fXMrShHgrXGEJOHykxjXRCOUplHxtkqiG1TyyH6gZp0nQ5PQ2BPGqolS/1AWyWovQsaSMYoQHNHY/2ZuG6oAY3XlP70v1RTp3fR60OBNfI2SeVPwfnpbrjvLTNKdGVd45ok1iGXLm60kKXviEeY2F7KJw3ZWrlG1qXYp2JdUD7WEq+BOh7V/4kyJ9jfbLEOhQXlW2IDlL5c5BfTt4hdOWdo8tHcuXqSltTrlp9d9GV79T4hT8rYUeDZylGFmHRWL8nTF0PU5ePxw54XolprUOBKSqm2+KUE1O6PMPE83+1LNEH7U+bYX9aH/vTeDhgXDjW7wlT18Mc7MSzVwRLh9LJYZPPnz+fPo9Gh3fp0qXBdlqiD9qfNufQ/Wld7E/jsbjX6hhjdgMjA/w4Jj4cvlTozGnAY+fG6MjSOiozHofuT2Z+eIRx4Vi/J0xdD7bTclmibe1PZizsT+MxiYDRGGOMMeZQccBYge8Ed4v1e8LU9WA7LZcl2tb+ZMbC/jQefobRGGOMMcYUccBojDHGGGOKOGA0xhhjjDFFZhUw8rJT5vq15ODFun1patD56Xc/f/3119Nj6cfbjTHGGGOWyGwCRoLF58+ft1snpAEhweK1a9eOnj592jxAeuvWrSbNkPdcKSAE8ohvpefY27dvm/3Hx8dHjx49auQyxhhjjFkyswgYCQQJ+gjUFAiKOMp39+7d5v+VK1ea/9p+9uxZ878PBYQEg1wn8tdffzXHdO2rV682X30giD2EF68aY4wx5nCZRcB47ty5M99u/eOPP9q1HxC08dkhIH3k3bt37Vo3BJ4EhIxOEgymfPjwofmfe1P9p0+f2jVjjDHGmOUxi4Cx9DkhjSaWgjYCyb5RQKaX4cWLF6fPKDKqKEpBJ8Fk+nwlo5UQ93sk0hhjjDFzZFY/ehHfv39v/jM9nBsNHIqmtZliJjB89epVs/3777830+E1MOoZp8o1Isr++/fvb+Wj68YYY4wxYzDLgPHly5fN/9zU9Dq8f/+++c8vognqfvvtt2YbdK0a4rR5/DHMxYsXHSwaY4wxZrbMLmBkxI8fmjAKuMsgjNFGGDqNzDOQoB/DMHp5+/btZp8xxhhjzByZXcCo1+bEUUC4fPlyu/YzBH8El4wgxucM0+cK4yt0Um7evNmu/cz169fbtaOjhw8ftmtHR69fv27+e3TRGGOMMXNmVgEjPyQhWIxBmZ4/JCjTqKCecRQK9ggI9WoeLZx348aN5rh+ZR3RMQWFuRHHNFjlmUXghzT6UY4xxhhjzGxZBU2jUivCKghr0qbLrVu32hT//HN8fNzsWwWVzTbH2F4Fgs12CeX36tWrJj3rqwC0PXqC8gNdC7lSus4fA8l76ExdD7bTclmibe1PZizsT+PxC39Wwo4G08J9IuS+8iLSEUd97UUMKR5T1hplZLQyN0Wtl3tDeu0IMjMqmU6d75sa/R4CU9eD7bRclmhb+5MZC/vTeMwiYJwjBIzb+hX3JrjhPGHqerCdlssSbWt/MmNhfxqPWb5WZ4rwbCNG1+t0eJWOMcYYY8wSmMUII2mWwBiqXuLd2DpMXQ+203JZom3tT2Ys7E/j4RFGY4wxxhhTZBIjjMYYY4wxh8ocRhj9o5eFY/2eMHU92E7LZYm2tT+ZsbA/jYenpI0xxhhjTBEHjMYYY4wxpogDRmOMMcYYU2TRASPPBehb06YM749EX3HhyzfA13PSYyzsXyqUb0jdIS1fATLGnCW2Lblv8Qu9y1aLMWZaLDJgVIBj6qCh5jOGPHSrhc8e3rt3rzn+8ePHM8f4fCKfTrx69WpzfEmsU3c459GjR+2WMUZwI0X7EtuNLm7evHnaxty/f//0IwjGmGmwyICRQIZGp9Q4mbOk37x+8eLF0e3bt5t1/RevX78+DSaXxjp158mTJ00HZ4w5CzdSjx8/btb/9a9/NX6SG7knqIzf7ucG1hgzLWYVMDJFqukKRnX++uuv9ojZBBryCLpF19qfHo/B5KHDNPSbN2/aLWOMoB2BOBPBJ1Pfv3/fbv0gtjEEj7///vskvsVvjPnBbAJGAhhGtTS1ce3atfaI2TZMQd+4caPdOguNOaRB5CHCSIlGT4wxZ/n27dtPI/UXLlw4M5KYwg2YZ4aMmSazCBg1kvjw4cPmP8GKG5XdURpBXPJ09BByoyfGmM1gtF6PhPg5dGOmxSwCxq9fv57+YtfslnQ6OiUNJhkR0GMCLBqBXDo8t8jzWSr38+fPj96+fdusG2OOjs6fP9/MBkVq2/J37941/w+lPTFmDsxmSro0jWG2x8uXL4/u3r3bbp0lF0xqREDLoUxVp+XmYf5bt24168aYH6PvMej78uVL5+MuET/yYsz0mEXAyIgWd6pD3otn1oORsvQX06IUTBpjTAqv53r27Fmzzg0n7YseLSpBW89NmANHY6bDLAJGGo1Xr16dmQJMpzoi3NEqDef4hcp18Kxo6fUwpWByKZTqjl5A7GkyY+pQcIjf8EPF2G5Hf6LtYV0L+FfSxkyLX/4ZeQ6NxmEdEZga5TmypQcwm7KufpfG1PVgOy2XJdrW/mTGwv40HrN5htEYY4wxxozDLANGRheZ2uDlrn55tzHGGGPMbpntlLSpw/o9Yep6sJ2WyxJta38yY2F/Gg9PSRtjjDHGmCKTGGE0xhhjjDlU5jDC6CnphWP9njB1PdhOy2WJtrU/mbGwP42Hp6SNMcYYY0wRB4zGGGOMMaaIA0ZjTDV8+eYQvnTDq7uM2TX2JzMnFhkw6vNuWvwpt3rQV+6b3XwHVvrs+qZ31Pvc349ZKqcYq57pk2p914zptKQNdzxG+hKc+/jx49Pv+9bUiUjNtehAa/KC2uvW6It8lAbevXtXTL8ram0rSJfTQZ9tdJz/u6a2TBxTOpZ9MUTnUb5UdzGfmgBpU38C6SzKHfNhqaX2ujHvrvqTyjUHf1I6ltpyRXSMZbHvh/5nZHYhwqVLl9q1f/65f//+Tq4xF2rLfnx83KRlefr0abv3hM+fPzf7+Q+3bt3659WrV826kJ7JZ4psQw8p26xnteciE/oH2SUHx1IbcW4sE8dr7UX5Yn41dSJScy1kq9H7EBtxvE9f7Iu2FKTP7R9K7po5amQVJR302Yb0OrfW/imcW8OQMqX+xLIuu5JPelRa6Y/98VzyLMm/qT8JZIrnpfmkcuUY4k8lHURSuYD1aON16SuP2JZtI7lyAfbsOqeG2jKNzehSbltRqZOVjH8IDNUvDpE2GmnjjS6j45N+Gw3BLtmGHiLbrme18qXXwC45OdMGDShT3B9tWiK1N/TViZS+a3G+6lFJ75GatH366ssjTb8O27ZtJCc/50V952wz9fqKf8UyDGXb8uV0SDrJyLqCE0i3I7m8yCeWN5cmhfToiTJIf1EmMST4zJVd9OlA5OQSHCtdo4Z921Z0lYu0fbbqo7ZMYzOrKWmG8OOQcW7Y97fffmvXTtBw/7lz55r/ZjhMJ1y/fr3dOtHlymFOh+UfPXp0dO/evVPb1E6nzJkx6pmmSa5evdr8h4sXLx69f/++3fqB5BGci/9oP77z/PnzqumTly9fNvaN9NWJSM21njx5cvTw4cN2azv06QtZ0Anbqrup/JTxxYsX7dbuGGLbPobYZpesW1+Rk8++/vHHH+2e3TBEvm/fvjU6jFy4cKHRNVy5cuXo7du3p/WbOtMl/6b+BFyH9OfPn2/3nPDly5efzkG3Hz58aLfWp08H0CWXmKI/bVquQ+r/ZhMw0rBjlFWQ2xj32rVr7ZEyVJzVncFPHaipB31HR5Euv3//3jjS6u6q2cY2qzuwxoHksIfCPuoZDZt0LWjY/v7773arm48fPx7duHGj3To6unz58qkv0TnjX13QcHKdSKlOpPRdi+cW37x5025tjz59/fnnn81/niNDvlu3bh3dvHmz2SeQHbl3zSa2TRlim12yTpmoC+k5u2KIfNQDiMHA169f27WTwIS2j/pN0IDPdLUFm/oTASEBYHrTCgQ1BK6x/d3WjUKfDkpyiSn60yblOrT+bxYBI0YBjUDgTLWNCiMXu75TPWRwJDpZ2QanwjbcRR8SU69n3NXfvn273frRIfGfhg52dWdcuhb/CdjGgI6Va2sUgnU6s1xjv61O15ThxoE6QhtC4DUVqLsKBjSSxHp6g/H06dPmf7p/m/BDjq62hvaXG1cGVCQn9ZxAclP6dFCSK2VK/rRJuQ6t/5tFwEi0XxoB6WLMzmhJ4ADcsQk5e9f0qxyNQF8OyLKrgGRs9lXPGIFI785rfIMAiDQK3HIQ8DKdVcvQOhGJ16Jhjp0b5WMfI02b1p+h+orTV/tmXdvm2MQ222STMmk6cJeBxVD5CAYIZll03p07d5r/1FVGrAkcdFNEHa6l1mZchwBQPqGZNs7XwArBjeQkEAJk30Z73KWDGrn2ybZsu065dnmzMDazmZIeOjUjY47ZCSwFHABnE0yT4DAEINy5xmc9BPujE7Js+xm1KbDPeqZrxE6UwCtONefgbvfu3bvtVjc845Mj18iW6kQNulasHyzkwSgNI02b1p8+fTEFzVR9yhhtxrq2zbGpbbbFJmXah6ybyMeoE/VHeTDSFGUmeCTQyLGJP6U+cXx83OwnyOFYClPkGvXcdnscdTBUrl2zLdv2lavU/y2SlRJGpUaElXGadPwaSaycqfOXX+zP/cLpEBlqYvQa9QzSv0h1H23DL86GXnMfbEMPKdusZ7XyIZOuUavrmjSUtwuul5arr06UKF2rRu+iJm1JX8jLNmWBXDlJU5K3hhr9wzq2zemgxjZKw3XWYZdlgnjeOuxSPtKsAop26wTyieeynaYRXC8t27r+JJlVh4X2I0ctNf4kcjqIdMk1dX9ap1xsS2+110lZ55wxGF3KWkWpcY9LzqGoIGm6rrSHAGWvQQ2WltRp5AgsuUYlnps2ElMAuWoo6UF1izTbrmecW0u8dtR1lE8gD/tTKJfyYCmB7XONfFed0H7tG3Ktmk6r1kaiS18Q25W0zgPn9snTB3nX0iVrWq6SDqDkr7HMueM1cF4tNWXahkwR8qilRr6o7y4fj/mUgqJN/SmiY5I76jGWpUQsG0uXP9XoQKRyCfKbo21FV7l0fu5YDZw3B37hz0rY0eC5gHVEYFifZ6HGGO6eE+vqd2lMXQ9Tl49pGj1mcCgwncVU4Tq/VI4s0QftT5thf1of+9N4zOo9jMaYceBBen7YM8YD7GNx6dKl7PNJxmyK/cnMkVkGjIwufv588k63Q3I4Y8aEkQEe5o8Pki8V2hju+Pf9QxFzONifzNyY7ZS0qcP6PWHqerCdlssSbWt/MmNhfxqPSQSMxhhjjDGHigPGCnwnuFus3xOmrgfbabks0bb2JzMW9qfx8I9ejDHGGGNMEQeMxhhjjDGmiANGY0w1fB/3UH7VacyusT+ZObHIgBEH5JkALaYe9NX1UXpeNtvl+Bxbks5LehBj1bOo677OJsqXlifmw3of2J53x+nVGP/73/868+6CdDonBx1obV41101t1KUvpdNx3hdXSr8rhthWoAP0JqJd4sJ+wI7psRr7r8s2yrRrkK223oF0mNMb+ai8JTbxp6jTtE0eal/VfS01kK4ko/KUvefiT13loi4qn/Q4r/bTsdQWS2ORASNvk+cBUpb79+8XncWcoMaqCxzh+fPn7VYe6XwOD+920aeHyBj1jMaKho9r8i5SXobbBY0ccinto0ePToOGmA8L6yX5OcaXleJH/a9du9bky/nv37/vfScqen3x4sXpNVOQ6e3bt+1WN5vYqEtfpIvQiVO2dP8uGWJbgS6wa+Tjx4+nZY55YTtsROcdj9+6devozp077dnbZVtl2hVD6hIoOLh3715TJl7AHemr42ITf2I/bbGuQdss313HvkPasVp9zc2fSuVCHwT25HN8fNzUTdmG8z58+HCqP9jnjc7eWRVyVLYtwqpitGsn8G3IlRO0W4fHUP2unKrzW5/s53gK+1O9T41t6gG2Xc9q5SMd3zMVXLNLTsoQv40at1mP+VCeLhlIl9qd68by5tJEOFbSJ+erfpXSRfrSpt+FVRljuYFykJZjqV1L+q1lF7YVfPeXdPH7v2kZyEP5pMfYLtmti32XaSi18omaekc5yDfVoaitu+ST6pzysohcGsE1om7i9lD7pulr27FSWTl/rv6UK1fajqh+QnpM5R7KOueMwaxGGONQO5F97g4svkmeuwu+BpPeBZrtwh3XytGWfWeVMEY9o86DRiXg4sWLzWhEDu7mkQs4F//Rt2tXDfnRt2/fmnWI02IpL1++bEZUIoxi8C1cce7cuSZPdJGCn3Jt5JT/pukYbXn48GG7tR3S7/SqjMgqkI1ynD9/vt1zFo4xYrRrhtoW8Lc3b960Wz+IdROQ//bt2816euz169c/2XZbbLNMU4A6ugommraOOhzbu5o6Ljb1pytXrjQj8er/sK/anqH2jem30Y7N2Z+6yLUj5AXpMcpN/VgqswkYcUZNA+BIDN+XwJmXbLipQCODTViAhjIXdCyVfdYzArz0WhcuXOj8mD8N/+puuLEJnV3siNnPNIvo6tyAzozrRPDB2Cmo4/n+/XvzP8Lnz0DTOlw7Tk3tK0igXt6/f/9UVsrMdFLa6EcuX77clHXXDLUt023Rfl3IrmkgIehACUB2wa7KNAbokSDty5cvp30Q28gMfXU8sqk/EQS9evWqCe7wbfLb1L7baMfm7E9DQN9dN7c8DkJbu1RmETDqTkpGwjn6KjcdEI5LOpzK7IbYUKFz7sDv3r3b7lk+c6hn2CR2boDcNNzIzCJ/infk24Jr05Eqb9a5NgHcPoMEGvI4esKzSbWjKaWAet/kRk+6KI0wUSY60F3YfChDyjQGnz59anwkjuRx86HRslId3xX4NXQFpkPsu412bK7+NARikVJASIBeCpjnziwCxq9fvzYjjOvA3QDMtYLODYJ63UHiXApIWGLAsjT2Uc8YgUjvzku+wagBwTs2OU4e1gY6CC2MiNAB1kLHEqe0Ve443dtF7MCQidkC1RHKxz5k32b9SQNT8qaTV96asaBcUUf7Yoht6bDQkWTnBxAqS0qcjk7Z5XQ07KpMUyFOIacMDXyH+BP1kxFN/BrfBfwlJbVvjT+t247N2Z9qQSelEdR9zZSMyWympNcdSu4aqje7g0YCcKwYlHQN4y+BfdQzdUKxMWeK7MaNG+3WWWjANc3FuQSENHgpauwfPHjQ7jlLrpFlVIMGWDB1ht1zeiAYZaomBZli/WAhD0ZOaHi3VX/UYcVOPM2bgBroZMYYIRhiW40GacGu6Jj1CKNb2K6rbpaCyW2wizKNhaZS00BKo3ulOp6yqT/hw3E/wSP+m5Lat8af1m3H5uxPtWCjrhFU/ZJ66cwiYKTSU/HWGWHgHBqfdR3BDAPH0fM8h8S+6hnB1LNnz5p1AgJGYroCKTocHrAXjB6koyLYi2eh8K8u2dmfBpoEl4wICUYyu6ZqOEZaNd5cE13tA4JFZI864vq1aCpyH+3HENvWgO27Hg/pCya3xbbLNBboiaAw1h38Rq+rGVLHN/UnfnCBHgWBKrJF1rXvrtuxufoTo6Zx0Ao9RVvTrsYgdZ1YZRas7gZGpVYE/Vw9LulP2iFNt6o07ZHDBB3UsAoYzuht1QC1R05gOx4nPaT6ztlkCiBbDSU9rBrSZh9ptl3PyKMWycEiO0CUTygdS5RR9kztnKPrFR/sz+Wt/XFf1FfpmlynT5e1Nop6ikuujkrmqDsgj6naVnAsp9PSdTlnE18dq0y11MrHtSUbS1ddEhxX2lR/tXV8G/4UdZrLi+M19o0yp9eAVAclfUXm5k9d5Yo2yR1H97njabn74Jw58At/VsKOBpH7OiJw98Qd2BjD3XNiXf0ujanrYery6S76kPyNkQKmoTb9ZeUSfdD+tBn2p/WxP43HbJ5hNMaMB8/u8IzOGA+wj8WlS5dOfwRgzDaxP5k5MsuAkdHFz58/N8+QHJLDGTMmjAzwfKqe3VkytDHc8e/jWStzmNifzNyY7ZS0qcP6PWHqerCdlssSbWt/MmNhfxoPT0kbY4wxxpgikxhhNMYYY4w5VOYwwugp6YVj/Z4wdT3YTstliba1P5mxsD+Nh6ekjTHGGGNMEQeMxhhjjDGmiANGY0w1fGD/UF4DYsyusT+ZObH4gJFvOuKUpg6epch9BxMdcix3nHdh6thSdN2lhwgNvcrNsq+Gn69E1FwzptMSG+6STXNwLi8b1rvU+D7rkPOVlgXZRKw/QzoW0tdct09fHE+vywuGu9LvklrbQl9aHWOJ+haco+O7fJ/tkDLFusBCHdsHXGtoHc7JFo/ndB5J/Qk4p8YHSr7H+VEOlj5ZavUe6wxLjnhc1527P5XaymgLlkXzz8jsUgR9B7L0Xc+lU6vfru+YAt/d5Dgonb5TyjbHBd/WrPmG6b7Zhh5S4jdc9W3Sdak9F5lUn/X90xwcS+3AuSpTyaY5SB+P69r6Zioylc7nmK4XydWfPn8dYqM+fXE99kVbCtLn9g8lvWYXtbaFvrRd+haqr6U0JXZRJoh1YRNq5RtSl6gLquMqS9Rfn84jqT9BqS5GdG3+A/pVXvzXfsHxPrlq9R5l45z0vJIOkGuO/kQZVSb+c0z6VlrpnP218kXWOWcMRpdyl4qiAmBsVYRDZKh+cei04ZRzCOkV5CiC/Wn6KbANPUTSMqrhqO0wUmrlS6+BvnNypnYByhQbtki0aQrXSxt60sb0uTSRrrxTOdiu1UWfjaBGX2x3yZ5LP5Rt2xb60nbpG0rlrWUXZSLdttqOWvlEX11CtlRnpI96Luk8kstL1NiG68RrxfxSv2e7L79avad5c05a5j4dcLyk5xp2UfdKaVPdxLYyrQPA8aH1eGh9HYtZTUnHoXaGgUvTKAwhv3nzpt0ym5B+IJ8plIsXL56uC+zBUP4hfFA/pxM4d+5c838XaJro6tWrzX/ADu/fv2+3fhDtApyL/2h/yaYpL1++PLp37167dQJTTNevX2+3Tsq96lCy0z7Ui+fPnzd+m/psKsf58+ebb85ugyH66oIyvnjxot3aHUNk7Utb0jc8evSosafa0pqp2HUYqv8nT540n3tFpqnx7du3pn5HLly4cPpt5D6dR3L+NISS76V+//r1695r1eo95s21OIdvYosaHczRn0pt5ZcvX35q8zj+4cOHdmtZzCZgpLOj4q+C3MY5rl271h75GRpAng0xu4EG6+HDh+3WCTQSNCCHCo3O6k7zpwZ7m9BppcEUnRbfpO3j48ePRzdu3Gi3fiZnU8ExrhPBBwnuhMr9/fv35n/k8uXLp35LHSk9o4WcdGDbYBN9CWRPA4VdMETWvrQlfdOR61zSvHr1qgkg1Wluk6H6J/hApqdPnzbtya4C2XVApxBl+vr1a7s2rI7n/GkIQ3yPoOfKlSvtVp6hemcwJrUr1Ohgjv6UEttKAuC3b9+e8Z/cTfNSmEXAqLsVGQkHyVVYyN0tmO2BLXIduhoKnGcpP3wZAjqJd9tTg7v627dvt1tn6bLptlCHxn/qCXR1SnRwUxyhnlMnUNI3Ix83b948bUvRNW0po15jI7mR7fj4uAlkp6J3ZFNwTVDFwjq61HH976vj+wLdEfT09YVD9c7MHWWk3qAHMUQHcw2q0rYS/2GggAEs1Qv6wDgCvCRmETByJ1e6Y4tgzOjUDJFjwFixzXrg5HQ4XR06DQWNqu7McC7ZgWXsBnRXUK59jGgzqpDendf4BjdRpFGDHumzaRd0FtyZC3UANVPy+ChTOSnpYySb1p919TUGQ2QdWq4ufQsFPdtmE/0T5Ny6dSs7ajYW+AiBEIvKdefOneZ/Sp/ON6HW99Lp6Bp/GqJ3RtqgK/jbpQ762IU/dbWVGqFlof+Doe3pXJjNlHTX8HCK7n60EP3jAKybzaBj6RtFw/lEbGBZNKqxJGiEYR8j2rpGbKBpkEtTzcDo0d27d9uts9TYNNfIch6NqqCDoSPLBaU59AyQ4JUWadC9af1ZV19jMETWdcolfTPyoY4+sosRkW3of5fPBG8C9ZV+RWXMkdZxURMwl6j1vXRWYYg/1ei9xte7dLBrduFPNW0lU/FM7S+WVcUZlRoRVtF/k06/WoKVg1T9EolfMPGrpUNlqInRa9SzSPMhDXZJQdc1dtk329JDhHKmv5BLt2uplQ+ZdA1+1VdzXleaWptyvbRc8klR649A2gh5x3PJu0/3UGOjGn2RJpVJIFfXsVpqbAQ1soohaVP5SSu91dahlF2UKYLeJeM6DC1TTV0S5N3Xp5TqDPqQTlKQoa++1fgeul6n3xui92jbHLlyzNWf0m3Sx7ZS59TqLqW2TGMzupS1iqKikTYuqZPkoBI4YOxHjZAW6UyOkC46jn7jftJPEWSroUsPoLKSJi23lpo6mYNza4nXjo1WlE8gD/sjfTZNIX2ukY/5xIZS+7WPfJWOJUK+8ZiWWIaUWhuJLn1BKlt6nHPX7QQE+dbSJeuQcpX0LeLxtMw1dOWbo6ZMLErDwrFNII8a0ut21aWYLufjNToXXf7UVRdTf4Iu3xPIXtMW9ek96oD8Ytr0ujU6IL85+VPUc1xUT6JOYl5D4fw58At/VsKOBs9SrCMCw/o8I7HUZwW2xbr6XRpT18PU5WMKjmnLQ/I3pqiYhqp9HKaLJfqg/Wkz7E/rY38aj1m9h9EYMw48u8Mzhnpm8xC4dOlS9nk/YzbF/mTmyCwDRkYXP38+edfTITmcMWPCyMCff/555uHwpUIbwx1/7Y94jBmK/cnMjdlOSZs6rN8Tpq4H22m5LNG29iczFvan8ZhEwGiMMcYYc6g4YKzAd4K7xfo9Yep6sJ2WyxJta38yY2F/Gg//6MUYY4wxxhRxwGiMMcYYY4o4YDTGVMP3ng/lV53G7Br7k5kTiw0Y0w+t/+9//2uPmBLoKvdReho26bLrVUY0fH1pIKZjSRtMbKVjqSzxGMuuyF07x1j1rFY+6ErLy4MlN+t90Ojz7ji9GqNkp5R4rVznEY+z1EC6Wh0A1+UclTWtS1rYz/viWN93Zx71ULp26kMpJduk5WZ7l9SWKa0DLLsONFI99tk7po16S3WaS5OS+hOggyFlxra6Vg7a7RofqW3H+uodxOPytan7E5TSRvvm9Km2Zdf1dXT+GZldicBnfky9fkufmkKXHAelS9FnlZSuRPwsls4TfF6JbX1miU8w6RNX6TF9lqmG2nQlPeTYVj3bhXyltGzr81bAeqksHIufGivZKSW1U+5aQ/Q41Ea6fk4HEcoS62a6vS6x7CWiTaTfLlIfivrrsw3HtJ3appZtl4ljUUbg3NRGtdTKV2qLUkgrGVUWtXd9dSmFa6XlJT15ls6L9KVFJtLU6LDW/+L10noHlEk6SenTSS0lG0Uod60/ldJqm/+Q+hPb0jH7lc8Qass0NqNLuQtFUWFTZzxUhuoXh04bGDmKSPMkfW1DkNpFzqhGJm2E2K+8uU7aQKXO28U29JCyzXq2C/lELi37YsOeNpKRaANRslMK146NaG47rWM11OgAuShXLv90H3ml+VHGvmv0UWtb0kWbdF07lZs6GG1Rso3srDzS7Vp2XSZA5qFyiRr5+tqiSNShoBzScyonx7rqTS4vwTldxyKk6cofuIbyKqUD0ta0Y2kZ03oH6XZKl/2HsO26B6W0rMdyRfuxnsrDsZhXDbVlGpvRpRyiKAxBehm3q5LTGSndoTNUB30NTC5A4xqcI50PbRA4R40R14/5qxHnP04bAw1InbkL8hhCnx5gm/VsF/KJXFqul7NjrqFDv+n5JTulqFFVes6N6TjGktq2j1obkabmGqlcgMzs3wSu20eu40HuPp1I75E+23Bc+XKNPh3m2GWZgHNr0nVRI1+OqKcI+kzzLNWNXF0SOX8S7O+rb1wX3bAgU05m6Y68+uwb86lFdSoiHbHE+hcp6ayWGjmH1L2+tMgby6Oy8z+XJ9t9Ok+pKdMUmM0zjDwbcO/ePbTafBbw2rVr7ZGf4TudpFsZrfOZAzMMPePy9u3bds8J7F85VLOOzleOdfTo0aPiszsR0q0a0NPneLDt+fPnm3XQ/u/fvzcf6+f6Me99PxMTmXM9WzVqzfNToqRHnj+6cOFCu3VCyU4pV69ebeoFn/JET+Sn9FwXHbIAx2vrTh/kTX358uVLkz8ys52zk8ofnyeDy5cvN+ftmm/fvp36kUDnfD6uC55PS8+BPtuQJ3pA1/Dw4cPm/7ZZp0zi48ePRzdu3Gi39kPaFkWoBxDrztevX9u1s3TVJZHzpyHwOUHAf6nX+PLNmzebfUC9ePPmTbvVz9B2rKveoSP5Gb6ee55viv7Ul7bkT7QtqZ3ZZv8SmUXAqB9QqGHDILkKK2RA0q/uHpoAptQhmn5+++2300YlfsP7w4cPTWMl25AO27x8+bLZ7uPJkydNg1UDedOgc7NAw8ZCx0cgOQZzrmd0KDSE0qP8ieBuV1B3IHZusbFFJtLcvXu33bMZnz59asql+sW1qD8vXrxotiOvX79ubki7mKJd0Rc+SRmx4RAIMliG3NztE2x0+/btdms/lNoi6o5uhuUzrMe6LPrq0qbQ5hEsyldZx5exI8FevBGsYWg71lXvlA//OQ5dwaf743kyi4CRO7l1f32EU9Ew5kY+zHBoVNBn1901qBFNf3mXNh65xo1GiDs+oYbl3LlzzX/dDbPQgAOB5NjMsZ5JjyzITjBVS5+dItQDRkWoO1wLGKXIQRqNQPTVn3XourkYI0CJMIKRjrzUtnuMWIFs0GcbdIkfKUDnBmwXHfi6ZSLwIY0CkH1QE2jppplF5bpz507zP7LvuhRv8gj24g01crIPf6vxpyHtWFrvUgjAxxppG1L3+tKW/OnixYs/lZ9t9i+R2UxJ10xjlMh1ZGY9YkNOB6yGI8L+2MCyEAwIjVDGxg4INmMwSsOFw+Y6D0Y6NWo1FeZYz7AFoxYPHjxo95wl18gOsROj0HE/wSPX64J8oFR/atD0V9qgp6NCYwQoKfKDKCudbc20bCp3yTYaTdQ56JRjjMZum3XLxOzEtkaZa+hqi0rwChYCq/ScmrqU86chcF2m7FOQJfoLC7aljeTmYIg/1bRjNf4yVuA0pO71pS3505UrV36KTdhm/yJZVZxRqRFh1eg36VYVv93z84OoXZAmnndoDDUxei3pS7aIRNvkHiBOwSb3kx+qaDvNP2dnXWOIXftkSunTQ8qm9WyX8pXSoneujd67IE1qrxo7Ca4d07K96vTarbNwHexbQ40OuE68FnKk+XPNLtnZz3U2oda2lEV6rvEjEc+Dkm3+/+2dva4UudaGe24DIYQ+zjUQICAgAC6AgENEhESOIJlwkjOaeJCIJmJOwAXABARwRMA1DBJCiNuYr5/a/e69WNguu7pr10+vR6q921Uu1/L6sV12dZeO+XTJ/inGrFNt2SVqy0APVnfg0x7KLvlvzpcEeXLXQF99/kb5yCCblcqriRELZdfm937nSdWD8ucYT6W8pXgCfEFpysn5RonaOk3N5FLWKkpBYjdrNCHjais59DGADmrwevOdrLZcsNs8pc4He9i82qwtFbBstvGyPlC6RgrOqaGkB8lOHp9vXz+jjBpq5YNSXj77fTmwR8ruOTtpv91n7W7L8nGdimlPiw5AdWVLlc/+HJRn6zGEUvkeqydbB1svr7OUfDnbgD3GRroVzqulpk6CurF/X2rks3LZTT5i5WPzx1NwvA/0nYon66ds0ksqnqwPlGKY65T819aLzeve6qDP77z8KSivJE8NubJTWBtLn2DrJXJ5oRRPgJ45lrJrDZy7BH7iz1bYyeBZiiEiMK3PMxJzeH5tzgzV79qYux7mLh9LcHrM4FhgiYrlqH0fh1ljDEY87UfE03AinqZjta8GDILgcPAlCb4UoOe9joErV64kn88Ngn2JeAqWyCIHjMwufvp08ltPxxRwQTAlzAzwhRX7cPhaoY3hjr/mwf4gGELEU7A0FrskHdQR+j1h7noIO62XNdo24imYioin6Ygl6SAIgiAIgqDILGYYgyAIgiAIjpUlzDDGkvTKCf2eMHc9hJ3WyxptG/EUTEXE03TEknQQBEEQBEFQJAaMQRAEQRAEQZEYMAZBUM3du3eP5mdAgmBsIp6CJbGIASNBxRp/aTsv+N1HXfPXX3/d7Q3WRqt9yYufnhc18tERyVfZfMfE2yZ0jM990OjzY8P2t9Q4r6Yz+N///nd6LS+3jakWHabKKoGcnOPrmqoDPzBM3vPuzK1N+q5dymvbTK8jawu2Y6dF5yLnS4C+a3Tr46kUIzmQl/wpuXWMre/3ilt0oHxsyJzC6+CY4imnk1Xwz8TUimDf5ejf8zj0/Y1D0Tsz930n5nkwAxPPgiF+VmtfnVN6p2sfY8hn40LvSRWca+XlM3lycMy/Q5fyKbMv/ohX8iluuZbKoj72upRVelcvtNpI78BN5S3VAXn76lYD5ddgbSKd5SjlRZ/oCKQr6dTbQrppZcg550mtfC06h5IvAcdqfMbHk7eLjZES8l+dJyif/fKDEi064Hrel/w12Hcs8QQ2zf9a+SxDzpmCyaWsVZQMxYZBpwTHQo5cozEnluKIY9OqBxq2WvviDzQqanCGcGj5fGfjG3fOtw19qUElX66hR4a+TgDdsAlbno9l8nnZc/TpANRulNqMUh2Qp9YPctTa1toHStcu5fX6k38CefRZcLxW56K2TlMxhs77fKnGH4FyvL9xXWuXVB4P+TVAsTIhQ9+5llodpGTy/tSng1zZLYxh21LeUjyp3ZT+fbqW2jpNzaKfYUwtX9mpYaHlAza7/MXUsZ06t1PJdgqarW9K3+Zf9ZR08B3Y/fXr17vUfLh///7u0wla9rpw4UL3f9ugbb5+/dp9Brss5nn58uXm0aNHu1Q7LEfdvHlzlzqRgeuz7GOXt4kx9nnZ9+GXX37ZbBv+7j22xGaqzSiB3C9evNilxkN6v379evcfLl++vHn79u0udUZf3pTtOQ6fP3/+YbmN4+/evduljocWnUPJl/Bd+hnOVT+QWwJNxVMpRlJwPfJfvHhxt+eMZ8+edeVLjtLydosOaC+QyXLp0qXT90PX6GBt8cRn/EGPJrx69arzEduurYlFDhgVsG/evNntOQFn5dh2ILzZ3gGcBjTv6xQ0jNs7hu7zjRs3uv9K0yAAjk/ZlKEA4b3VOXAW8pOXslRusG5oiHkGaQnQKOLPasi2d8nfyZ7rmIAOgY5hKMSF7dgkw7dv37r/QDyXYmwI1Im4ZJBEm4AcpEsdqOfq1as/dJJjQGdM22VB57xv2NOSF7Df06dPu8902OhAnSSUbL9mWvTY50vqY4gpjhNft2/f7vZ5UvFUEyMCWejHUjdW9F2qE3LQHzGAtPa2tOiAWAAbP1++fNl9qtPB2uIJyIcv0IaBPbY2FjlgxOHkkIJAYb8clDsAjOgbw99///00MHGSlHEJRMonr3ckD+U/f/68y0eQq2zkCdZL6q50znAzhD8LZkWJF80EyM+nqg/xhjzEbOssYI6PHz929VK9iU8GzUNmOJY6qKId0o0w0LahA25qZXt0bme3gh/p8yV0yEBJ8cNn/Dk3UNsHJihsLFsYSNIHql/D3sjNrOa+UGcNQOU7fFaf26KDtcSTYCzCVhqcr4FFL0mnlgIZvMmRIXWHlsPeUXAXRTk4vCg5uTpfzS7aO69gfdBo2IYTv6PB5PPcyM2EMkjTRmNHBzgGdFjcxQvFkZbHhTokxSGNs/TL1jIzmGOuAyNuNG1bA7QhrJp4avOi59RMFIMN2V2rK6nZqrXTovMUJV9qvfGqjRFiQu0Mm/obzs9NUmhAl4qnVh1oMoVN5z148KD775nyZvo84wldElOMR1iOxialscKSWfSAMQUdnxyabYjTElgMBlo7UHvdNU9LByc3K9be+Ip8b06oEynFgTqhJ0+e7PZ8T20HmoMOiwZYcBNHB8cA0UPjLWznxNYaU1r+8o23OtA5IftYWVn+vHXr1i51Rm1e6pmbiRI8BkAnd4y06LzPl4j9Dx8+dJ8tqbhLxVNtjPiYeP/+fbcf2TjGIFbPFFrYn4qnFh14mOmk3iqjRQdj01Kv2rypeNJsouyETrEbM9KrZOs4k1IrAt9gIi/bNjh2e79Hx/Vtp21D2H2254LSW8N26e1d9ndpziO9HQQkr7sNjC5NvlSac/R5apAraNcDvtBiQ3wFPxjKGPLh18hlSaW5di6mgDz+PIEMipsclG3rR35kS4EOc8c8NTqgPGsX5CA+LaU6IEtf/fqotS1ySM9qd3L05fVp8lsb65w+/eWordNU1MrXovOSL6kPkY5LMZM61hIjFslsbUtadu2rE7ToQJDH6gJqdLC2eJLdZCulrT1qKMk2JyaXskZROCb57OYdEWRobQoaHNSeZ/P4sjnHlsNxnc9nBYU2OYbd5wNpSpAnqNeDAj5lS/lOqjHg2D52P7R83s+1qWHjPNI1MhMPqUZeZWiTXhQ/ij+wMWX3eznJ10erjaycqr/I1UFQnpV3CJRbi9WHlSVVr1Req2e7SUe2/fJ1bYHz50yLfC06L/mS1a31SU8unnIxov0pP9Qxb0uVkzqWokYHbMrj6y76dEB5a4on8HlIt8J5S+An/myFnQzW/ycWYdWEfk+Yux7mLh/LT1rWOhZYomIZKvdtyVrWGIMRT/sR8TSciKfpiAHjygn9njB3PSzBTjx7xZd9jqWTwyafPn1KPmvZwhpjMOJpfyKehhHxNB2r+9JLEATjwMwAv7VmHw5fK3TmNOD7dm5BkCPiKVgaMcO4ckK/J8xdD2Gn9bJG20Y8BVMR8TQdsxgwBkEQBEEQHCsxYKwg7gTHJfR7wtz1EHZaL2u0bcRTMBURT9MRzzAGQRAEQRAERWLAGARBEARBEBSJAWMQBNXcvXv3aL7VGQRjE/EULInFDRj9C9TliLxIfc6BR8MgmecmJz8ia3VKWli57f61Q33xqT68P+rdomNTIx9+ZmXzWLvX2JZY+/nnn7/7aQzOq+kM0Iuu5eW2x9hqSZXlsXXUZuW1x+1+3sfLvvOOVStP37VLeVv0fV4+O2dSeiqBr3AONhC2rawpa6x4AslXU5ZvJ2p9vk8Heo89LD2eQMfYbJ1tH1Cj70Xzz8S0iKDXMtnX8vAKH/ax2df8zJEp5OR6tZA39dqm0muulkKtHuxrnmpeYcXrpA7BGPLZ148hp5WVc61d+VyqC8f868D0yszUa84silH5PddSWf6YXi1WolYHlOllJr/O8dfyOuD8vrrV0FcfYW0iveQo5VWa/2D1DRxTukbfKYacc57Uytca79KXz4vf6JVwKtPq3DNWPAFp6+Pykxz2eshF2SVadGBB3iXGE1Bn1c3CPttmUL8+faeordPUTC5lraLkyDmH47gCaK4gP9t5ytniiApyGxgEUipQlkaLHgA/8w2iB734Rn8oh5bP+xhy+obN2rXUoJIvF3fIkDsmuK69ti2P8+0xoMGt0WurDoBztJ9zbePu04BspWvUUGtbH3ula5fy8tnq1OpbdpYOfLqW2jpNRat8fb4E6DGnK++v+JG1gcXaw4MMuWOiZF/JaOEY+1N4ueUPufwqP6UDvy9lA+ReWjyB1bfF6490rXyWIedMwSKWpJkefv78efeZVyml+P3330+n9v0yYWkqmul85bPnMeXMeTZt0XS8P2an5MF+Fq9evTrd37IMMjbXr1/fbBu6zcOHD7u09MZ+S24KXrrkv9fXGsEX//3vf3d1nht2mQs7IicxIraN++br16+71Fn+1PLky5cvN48ePdql2mE5ivfmigsXLnTXR67Pnz//EJ/I8u7du11qOFYHQN3wV+2/du3a5s2bN6dLZy9evPhOR4Dc7B8b6d3G2uXLlzdv377dpc7oy1vSN3XfDiBO45O2aNsx/qCr4EeId3SF/oh52nrhX++HPrFJijHj6cOHD10bbsHn2Z8iJTdQZoqSDqwPsX87eNqlzlhiPNE+MP6gvmorhNffxYsXO92slUUMGD9+/Lj7dGKQEhifzhGn3g6Iu+ApGfDp06enxzmPcwAH4UXpNq2OjWAgQDm2vSvpjmngZzscAjUVNHSSku3Zs2c/OOGUID91oz6//fZbpx8LstKZI790o8E1AWb1tXbQFfXF1zRQnhv4asr/8T2enxJ+0Gahg7p06dIu1Q7+ZONWHcu3b9+6DoRBmxptKMmyD3Sat27d2qVOOgjiU4N+6ukHTlevXu3kHxsG795O6JzXx3n68pb0DeRD59QZfIwHP4JPojO13eiYdC7m8aWcXseMJ+TzPkya/TUQh48fP/6hDKjRAf0DfsX+FEuMJ2RWfWkr7CSJhzYmN6m1Blb3LWkZSwEpx6/pzDW4k/Pwnk8PAUUwKI/uxBj4eWgYuAPRIEo8efKk+6+ZvNR1pgJ9MQCiPg8ePNjtPQNZdbfFRhBRT0Av6IeG0td5jci3qC83DuhsrMHOUF6/ft3ZAn/VAAHYj+1kR/mzvcs+D4gPOqgbN26cyoIf2RmUQ8HMxr1793apM/B34AYxx9zsui/cMOiG1Q7WgzRMWhAjmhAg9vHb1GwZg6alDhqQ28+yixodqL8jphhc5SZDlhRPauf5r34tN55g0sTPOq6JRQwY7d1Ubmq9D+6KmAlTp8TWumyqO/QcPgjkaEsj1alaNHurjTsx6sp+dfxznG0bEwZadMB9PjIVGtRbH7U2RHYa/zGgk7HL35JBN1uaqWXTTRuNrmYrtO3jUwyK7HI0UD43QPYGxy6xnSe0cX7m5cuXL8nZjL68ffpGl+icmwbF7JI68LmQuqlBj6zAjDloKNmXpVRvS9Ls74sn0nbVoYbcjR0xRZuCX07BIePJw6A6NWNL20FMrZlFDBjVGUPqGQTI3ckIAsZ2TGy5O6mhDB0gLm1g6YNFA291vHT6c1tqPy9yz/5MTcnHsBOzepr59uQazlqYubMdB4NqOr2UTHqcBDRboQ3/GgrPjWlGX9CxWxkYPOaW0sZGM7u2syfO7BK66Mtb0rdmE1VvdMox+9hP8CNaSvWDMT8rTbqvXxkznngu1y+7kmZ/KZ7UVpdWGGp1IFLxfV4cMp5SMJ6w0Ae2DrYXydZxJqVFBPKyPXbfWCK97WS6z/oWl9LbgWaX3jp6l06xDbYuz3ag811a35ris02rTND1JBPXUX5/Te335ZRk2xfKb0V1kJwWdMQx6Yq81J3/1EewT3nmQKse8AH5UA3UtSW/Z2z5yOvjBtjHtUs+SJ7UuUC5yFJC/iTI731DcdRSpxYdpPTLuXY/aevDgJx99esjde0UXF96lj5ylPKW9K1jPl2yf4qSbHOgVb4aX8I3rH9wDdtG+mtSXkqvY8cTMipNed6nPeT18uTk69OB8DIKrrW0ePJ4+TnP6p+6U14LtXWamsmlbFUUhuAcu1ljAWl7vNQY+vIwvk0THPpsHYXP2m+Dy+b3gco5CiRtJdkOAddoIaUPj80jnVCPUt2nBplq8Pax9ZBvkMfnS+mpBcqooVY+HwO+AZOtauxEA+obSVAZ2rguqMG119Q+v9/KqfP7qNWB4Bo5+yg/W6qOHPe6a4Wya7Hy2Dqk6pXLCzl9gz3GRroVzpsztfK1+pL1efU7Xp/acrFF/rHiSVA+x1LXsVgfspvqVqsD0D623HUpb2nx5O1ikZ79Zs+vgXOWwE/82Qo7GTxLMbEIqyb0e8Lc9TB3+Vhy4XmlNT/Q7WGJiuW21DcrW1hjDEY87UfE03AinqYjBowrJ/R7wtz1sAQ78ewVD3wfSyeHTT59+rT3s1hrjMGIp/2JeBpGxNN0rO5ndYIgGAdmBvhiiH/ofY3QmdOAT/ngfrBuIp6CpREzjCsn9HvC3PUQdlova7RtxFMwFRFP0xEzjEEQBEEQBEGRWcwwBkEQBEEQHCtLmGGMJemVE/o9Ye56CDutlzXaNuIpmIqIp+mIJekgCIIgCIKgSAwYgyAIgiAIgiIxYAyCIAiCIAiKLGrAyK/js9avLQUv1+/L4+F3sOw5frPcvXv3dP+vv/662xsEQRAEQbBeFjNgZLD4/PnzXeoEP5hjsHjjxo3Nf/7zn+4B0jt37nR5+n4Y9dWrV7tPP/L48ePdp5PB4ps3b7qy379/v3n27FknVxAEQRAEwZpZxICRgSCDPgZqGggKO8v38OHD7v+1a9e6/0r/9ttv3f8cnz9/Pi1bG68wggcPHnT///vf/3aDRV37+vXrmytXrnSD2GP4pf4gCIIgCI6XRQwYL1y4sHn9+vUutdn8/vvvu09nMGjTII/8lr/++mv3KU2qPGYdGRAyMIR37951/1OvNvr48ePuUxAEQRAEwfpYxICx9P5JzSaWBm0MJFtnAd++fbu5ffv2LlUedDKY9M9XsnwNdn/MRAZBEARBsEQW9aUX8e3bt+4/y8OaATwkDOxYftZydA3MUtqlcs2Isp/nIBm0xovXgyAIgiBYIoscML58+bL7n1pKPgR+OboWu2xuvwxz+fLlGCwGQRAEQbBYFjdg5AswfNHkzz//HG0Q5pejW+Ab2qAvw/ClnHv37nX7giAIgiAIlsjiBoz62Zz79+/v9pxw9erV3acfYbaQweW//vWv754zTD1XmFuOLg0gb968ufu02Tx9+nT36eznemJ2MQiCIAiCJbOoASNfJGGwaAdl+lkdBmUMDEHPOAoN9v7+++8ffj7HD+Zyy9EaFKa+uOIHq/rtRn6nUV/KCYIgCIIgWCqLGTDyTCAzfwzC7Awhy8fijz/+6P5/+PCh+6/0kydPuv815JajmdHkSy3IACyN80UWBod+0KnrDXkOMgiCIAiCYG789A/TbBPCoK9PBAaL/i0vws846m0voqV6zB4yyOMtLrmBnt72Av7aFmRmVtIvnZ83Nfo9Buauh7DTelmjbSOegqmIeJqORQwYlwgDxrG+xd1CNJwnzF0PYaf1skbbRjwFUxHxNB2L/FmdOcLsJEbXz+nwUzpBEARBEARrYBEzjORZA1Ooeo13Y0OYux7CTutljbaNeAqmIuJpOmKGMQiCIAiCICgyixnGIAiCIAiCY2UJM4zxpZeVE/o9Ye56CDutlzXaNuIpmIqIp+mIJekgCIIgCIKgSAwYgyAIgiAIgiIxYAyCIAiCIAiKrHrAyHMBetd00A/60sYbczz8xuS//vWvXWrd1PrOf//73169BcExQ7uh+Ei9i1/ot2y1BUEwL1Y5YKTTjganDQaCf/75Z/fgLe/I5vWKdvDD8dzrGddEq++8e/eu05m2eHd4EJzBTRcDQbUrvHo1B+/wVxzxjn69BCEIgnmwygEjnTaNTqlxCs7QwFDvvf6///u/7j3ZL1++7NLw999/d/vWTovvoDfeFx4EQZpnz55tfv755+4z7QoDwdTMPYNK2hgRcRUE82NRA0ZmubRcQWfNcmCwP1+/fu3u/i2XLl3a/PXXX7tUkOKXX37Z/Pvf/47Z7CBIoBtRO+vOK1Pfvn27S53BYFIweCSu5vAu/iAIzljMgJHB4qNHj06XNlgyDQ7D1atXu//2zv/Lly+7T0EOOjT8kZlXBo3xvGwQnMGNqJ+p50bUziR67t69GytDQTBTFjFg1Ezi06dPu//cjUajcjjQJ88vsnykGVw+80xRkEezIvjl+/fvO52VHuoPgqDM69evTx8JiZn7IJgXixgwMtt1LN/OnQqeX6Sh1gwuPHjwoPtfghkBDTLZjnXAxLLbnTt3Nt++fdvtCYLj5uLFiz886lLblutxmLgBC4L5sJgl6dIyRnBY+HYig5+ab/xqRkCbfRbpGLlw4cLuUxAcN2o/7KDv8+fPm1u3bu1SeY69HQmCObKIAeO9e/e6O9V4Rmx8tAzEQDCoh8cm6AijowuCM3i+97fffus+8yUYfppLjxaVoK3nG9URT0EwHxYxYKTR8M/Y+aUOC3e0ysM5LJsGeaQvNvScGiyiQ3SJTsm31qWiku/oB4jJY3XGxu8x1nSEQXBMKCaIEb6oaNttG0/+B/AhviUdBPPip39YR5wQGochIvAcDD9rot8ODNIM1e/amLsewk7rZY22jXgKpiLiaToW8wxjEARBEARBMA2LHDAyu8jSBj/uGj/eHQRBEARBMC6LXZIO6gj9njB3PYSd1ssabRvxFExFxNN0xJJ0EARBEARBUGQWM4xBEARBEATHyhJmGGNJeuWEfk+Yux7CTutljbaNeAqmIuJpOmJJOgiCIAiCICgSA8YgCIIgCIKgSAwYgyCohjffrPUtPxZ+uisIxibiKVgSqxww+te2HUNAHgr0lXtnN6/yKgW+1fvSfx+zpAeLf6UZ78sdG71Sja3k2zafNms/ZNX+mrpy7s8//3z6ft+W8+kYc3kpV8e0IXsfqbJS2HJ9/lwd/vrrr27febcdtba1ILd9haVQOWxen943xqS2Trb9GFsmS4vOrXy5WFc9+sraJ54AuVPtsW2TUn5RQrHo/UV4v2GzMtjjdv8S4kn52FpsK53ZLae/xfPPxIwhwpUrV3af/vnn8ePHo1xjKdTW/f37911etv/85z+7vWegU45Z3VqkZ8qZI4fSg4d6H4Ja+ZDpzp073edPnz5lz+PYn3/+uUudwLmqk87lP1Cmz2+hnvZ4y/mcK7+QfpWX/ypDUFbJj1psRFmykWRW2X11YH/O31vgGjXU2tYiXeg8QT1KOtzXb8eok2+395FxLPnkH8qb0rHaSvLkoG7e1+w53hc9uob3T+SxerMylyAP5ZXiCdl8WeTXOSpDUAcrC+d7eYdgr1HiPGzLOfosuGYp9lLU1mlqJpfy0IryDl0y/jHQql8CItdosD8V8Ln9c+KQehD4lPe3odTK532ZBjklp2/EgDppP+fZxpwyczZMHWs53+vIdiReTtK5cjzk67MReez1bbqmDhzvu0Yfh7atRbpUxyhsvTyUmfKPFsb2V2xUqkMfh5Yv5Rvk8zKSRnbKzek452e2rFQeD9f3efw1JU8JrlWSV6SOc33tRx7rhz4NyJPSbwtzsq3XCek+u6WordPULGpJ2k79MmWcWva8f//+7tMJmu6/cOFC9z84PM+ePds8evTo1DY1yylr4JdffuleT0mdzwMtk1y/fr37D5cvX968fft2lzpDfi84l/jRfpaIbt682X0G4mPb2CWXbV6+fNnZ19JyfiomkVufLa9evfrhWvtw+/btzkYgHUiemjpw/MWLF7vUeLTYVrDc+Pr1613qDNrF58+fd36ZaiOJ122n1rxc2cpQf0X/2Oz333/f7RmHFvm+fv3a+Ybl0qVLnQ8JdI2/XLx4cbcnzb7xVMLqEXk438efh3ZsO0DqfAKfyfmFj1Xfply7dm3z5s2bU58jbrwN5xhP+9h27PZrbixmwIhjYojtILcz7o0bN3ZHyuA42zuDHwwbHAYCiYYGsM32DqzrkBSwa4bGkDrT2J7HQJmGTboWNGx///33LpXnw4cPm1u3bu1Smy6GbOOn+Pj27Vv330LDyXUsLed7KO/p06e71PfQoNPxHApsdOfOnc4+dIx2gFVTh6tXr/7QmYxBq23xNZ5/S4HMaicZeNF2CgYQHGMD9DJWrA7xVwYr/pyxaJEPnYKN8S9fvuw+nej13bt3vYMzOHQ8pcCuulEqgdwM8j5//nzqM6Rr2jLfpjA4o/3XTTT19P3uHOPpkLY9dPs1NxYxYNQdizoZnLC2UaGTGPtO9ZghkJjFkW0IKmzDXfTaUWNI3d+/f98NlGlc5gh39ffu3dulpoNYJiZToDsadDsrcCgY1Nd2hCnmZNfU7IlFfsl/DQxVb9uBM3hGLw8fPtztmR5kQmbaEAYdcwG96WYYudj4TNsHfMlhTv2MHfyVZpI/fvzY6VqyU08mWGpmAXNtCj4F0k2KOcXToWw7Zvs1FxYxYGS0b++Sa6GRzN2FB+OhQGNwoABkG9pZLwEaCWayhs4I1MAMhL87r4kNBhh26QjoJLgLF2rAax/dGHI+eUp36n455xD+Q2fJgMgO6nUDuq8ODkmLbRlw286N5WcGBnxOQX5mkFKgl7FmfIb6K2g5UDYZg1b58FsGYhqMwYMHDzp/kv7ZtPqFf8nX+hjDFzUQ0qxabTzZpfEcqTaF8v/444/Op3SjMvZjDzmmsO3al6NhMUvSqankEjLmmkf7c4DGRY27hf02CNk0C7lmxhxsyJdtJ8pAwC4LpWC2188iMainARUMdGkEbQcgUo1sy/mCc0p36n7G4hD+Q2NP5wHoj9kTBq0wpA5j0WJbzcJpo07crPA5h54ZTUGdx2Cov8J52GAf+Zh1QueU4f2UGxNg4MExz6HiqQb5PqTiSUvEVgeAPCVSbQpxZeVl8Ej8TcEUtp3LKs6obJUwKTUibI3T5fuP+YbTNpiy3/5iP99osvj0sdBqYvRq9WxhP8c91jZ842wGbvUDh9RDCnyuJb+nVj6uIV+u1XUqj2JKlOKJ6/n4aTkfvAzUgzIEddk20rtUHTU2Io+V3cpZUwfS7N+HlP5TDLEtcE5JdyX5OZdrtTJ2nex5QxhTPvKU9K1yrH9buJ6vW2s8AbL3+SZy9pVDHlsf5OjziZSekMfuJ+31NPd4Is8Q27K/dF4ftXWamsmlrFUUjkZeu6UCAQfx+XJ5jwHqXoMaLG3e+Unb4z5gSsfmAHLVUNKDfIs8Pp/vAFqhjFqsj1tdW/kEfp+TTY0fG41rDvKlGvnc+drPPpvHbt6/kLE2RmttJGxeX88+HVBeSTc1UHYtkp+tz7aCY1YHPlYtvh0d2i76ckvU1MnLNTedsylPn87kU7YsC8eHxpPwNta1bF3YOLcGW56tn9WB4Dj7U9jrp+rI8TXalvL6zi1BmUvgJ/5shZ0MngsYIgLT+jybk5ryD84Yqt+1MXc9zF0+lmn0mMGxwHIWS3Otj8N41hiDEU/7EfE0nIin6VjU7zAGQTANPHvIF8j8g95r5sqVK8nnc4NgXyKegiWyyAEjs4ufPp38xtgxBVwQTAkzAzzI7h+QXyO0MdzxT/EFmOA4iHgKlsZil6SDOkK/J8xdD2Gn9bJG20Y8BVMR8TQdsxgwBkEQBEEQHCsxYKwg7gTHJfR7wtz1EHZaL2u0bcRTMBURT9MRX3oJgiAIgiAIisSAMQiCIAiCICgSA8YgCKrh3bDH8q3OIBibiKdgSaxywEgA8kyAtqAe9JV6KT0Nm/Tpj/Mieh1jI710UvUsQYPIOfwg79hwDem61NmU4sDbTKJuuKcAACHWSURBVFvJdtSR347TT2PYMmp0hdypjoOfxlI5LR1L7XWhL690JX3ye3E2fV7U2hZKee2xkk6J6xY/H0JtnWw+bS3+sA9cq0YPVjYbK3ONJ+zbR4vesZ/N5+1p+wn7k3dLiCeB7r3eWvq/VfPPxIwhgn0lEa/sYTtWavWbey0VoD+9Ykr57GuQbJr/tdc8T2plKukhhep7Xq+74jp6DZxeaZWjFAdeXspKvcpLcK61ua6tV2Qhkz3uoWzy+2ugbysXx1W/HC02qs0r+VQf6NNJLZRbQ4ttS3l9DJLP6lhQBvn6dJji0HXimPcfzh0iG9TK1+JL+IJkVF04H/y5HC/5znnGU6mcVr3b63EdZBCkpQ/p1dKnk1p8uTmoQ208Ccmt8yBVL+8Hshv7a+WzDDlnCiaX8tCKkuEEBrQBdGy06peA9o2Fb1BsB+QDxqfnwiH04FHjcYi61spHPjVegB1ScnqZfBz445SRqy/X8w09ZdnyUnk8lO/zeN9qaXBrbCRKeamHruv1ktNvC4e2LZTy8t92eD4NnMv+Fh1aDl2nVAwh29DYqpVP9Okh5d/kpz4wl3hK+a+PMUuL3n055LH29eekbJCzfwuH9j2L+jYbL77etv+jPH0WHC/pPEWrv07FopakmSbXtC/TwKm3vNhfkmcKmrfB8BqmYDj+fafo+PLly6eftw1MN/UPr1692myD6Ds7rBXeZU5dqT8+WbP8sw9a3rp+/Xr3H7DD27dvd6kz+uLA2+fFixebe/fu7VLf8/Lly82jR492qRNYYuJduOLChQvd25dal5y8b128eLHT53lBG0I9uG4KjqGbsWmxbV/ea9eubd68eXPaPiK/bwPx3adPn+5S4zDUX4Fzae/n0o58/fq182/LpUuXTl91N5d4snJgf84vva+6Re+pfgCQFew5tIXbQdMudcYc40kg8+vXr3epM0r93+fPn3+wEcffvXu3S62LxQwYcWKCbDvI7QLpxo0buyNpMP55djzHBI2b7Wx4xRUdlJ7fGLsjmgM0EtSZBkM+SXrM58HotLxP02mVXuZfEwdq8FKdBGBvrmOhvnaQpXO/ffvW/R/Khw8fusHMeUC9adhLHerVq1d/GCiMQYtt+/LSSdJZc5NATGI/35mnOsZDM8RfBX5w69atXWp68AOw8f3ly5fdp++ZQzxhd+zfSoveGZQ9fvz4u3oySOXatIUp5hhPgF15nrQG2/8xAKauGqCCH0CuiUUMGHWnLCPhoH2dIA0iHTn5NJAJ9gdbpDr0O3fudNuzZ8++C5618vHjx863NHODT9J4nsfdcws1ccCssJ/xmApmAEoDuEPCrHjt6sMSOwFmv+H27dvdf2jpGKekNEM3BcQ3g3DaN+KIjc9Wt2IO8WRvYrlBqKVF7/QDPn6IXa6N7zFgVd/tmVM8pWYjc/j+j/rS7jOBJb9A53a2eE0sYsDInVzuW1t9cDcAax71nxfoMDUjQ5DQcDA4oaEgeMiru01tY86+zYGxGwlmIPzdeW1slOJgSOfMAJS7eKFytTw1BD/zNab/UDYNu8rWigX1ynVyY9Ji2768yP/HH390N9h03qBBA4Mc27lRDvtaBhW1DPVXOnDy2JmrOaDBkAZj8ODBg+6/ZS7xpEGuZtX64qlF7303HvgeEwjYewpafI8BoL0ReP78+WnbYMn1f/R98gstw/s8a2ExS9I1yxgp5tboLBnupv0dpe7OpGcaCho/ZuBsA8umGeI1oKUVPwBLzTgcCt0B22uyJF6zhJSLg5pOItXIUk/bGbB0ht2Hxhuzfb4DGtN/fNnv37/v9mPTKRr7Ftv25aVTs3Zg8EgHCLbObNiMm7wxlqiH+ivP+D18+HCXmif4KwMiPys1l3gSDJxEXzzV6l03VL7unn1l34cW39MqjDZmDLEtny2p/s/DrKpm9lfJVimTUiPCthHv8m0Nsdtz8k2u7Wh+l8rDOf5bTMdEq4nRq9Wz8OWQB7vINrKF0vyfE4fSg2XbqHz3bTquYb+V10KtfNafudaQ8yzs64sj8vhzZWdRE4/IQD6Pl4Gy+3QPNTYSfXmlS++3yJWSuYUxbFvKyzGftn5qadGhZYw6idqyS7SW0aIHys7p0/tyCvJIJ+KQ8WRBzr5yRI3OKMvL7tPg6yPmGk8WzvH29edStm0rVH6tD3lqZZuayaWsVRSORl67pQLB5xtqwLWADmpQgGtTwCgQ/GYDyuchPTeQq4acHoCGhH22oeC48qb8sRbOr0VyeFmsfLVxwLE+sGeqkbd2t+Vrv91n9cQmuSnX7vfHU3DM5i3ZqJTXIpn9dSlv3zaEcmupsa3I5QV7rNRBc2xI/Si3lpY64bfs35da+bi2ZGPL+ZLNV4pzjvcxZjxZXbNxbg05vVsd+LK1SR92X87nKGOu8SQ4Jj+wNrGbjtt2NlVWLZy/BH7iz1bYyeA5gSEiMK3PswdTLB8tiaH6XRtz18Pc5WMJjmc0jyneWM5iGWro4zBijTEY8bQfEU/DiXiajkX9DmMQBNPAszs8YzjFF0Km4sqVK6dfFgqCQxLxFCyRRQ4YmV389OlT8Wv7QRAcFmYG+AKFfZB8rdDGcMc/5YP7wbqJeAqWxmKXpIM6Qr8nzF0PYaf1skbbRjwFUxHxNB2xJB0EQRAEQRAUmcUMYxAEQRAEwbGyhBnGWJJeOaHfE+auh7DTelmjbSOegqmIeJqOWJIOgiAIgiAIisSAMQiCIAiCICgSA8YgCKq5e/fu0fwMSBCMTcRTsCRWP2D89ddfu6AM6uBZCnTmQYccY/O/fckL93WMbQ1Qj5QePOjC1h1djA1vidD1+jqbGvmop46XoNHnx4b1W2rW7n26sv6TyquyWvRXc10LeXNtgdcBPzDM5/PuzGttyzHlS+UtxauFfC06HMLQOp0XLfFk5fO+asupGSDtE0/A9VLXsTGf83eLlVtbTv6hfjf3eALlY2u1rdfhavlnYsYUQe+BzL0/9hio1W/uPabAuzX1TlLlE7w/k7Teo6l3a86NWplKekiBbg5BrXzIJH+W7kv0ycf5ufe+WijHvkPX2x2Z7HFLyn9sXurEPjblK9FqI9A5qbaA/SkdULca3fRB+TW02NbKhX5t3pS+U0jvtTq0nEed2IYylnzyW+WVnn27R5kl+Tk2NJ4AWcjv/RN57HWtzCm4nj+OTnI+4W1k60y65Hdcy8s7BF9ujvO0rU+3UlunqZlcyjEVJcPKaY6RVv0SOL6xUCMmbJnk9cHS19hNwSH04KFBOVQ9a+UjnxoyQPc5Ofvkq6kjUI5v6LmutXsqj/AypBpc30jXUCs/5NqCvjJK+q3l0Lb1+vS6K8WrIC9lt+jQcug6eZmpo/eRFg4tH3m8f5NPMvLZ+pZPW1JlUY6tbyqPh2v4PF6PlFlqA3x+oMzU/kP4XU6/LczNtqRT+mqhtk5Ts6glaaaCNeXLlHHfUsvr1693qWAf7Ouc0Ou24dilNpvPnz//MNVP/nfv3u1S6+WXX37pXk95XksQWia5fv169x8uX768efv27S71PSX5iB3iiXMVU7klm5cvX24ePXq0S53AEtPNmzd3qc3mwoUL3es6U2Xcv39/9+kE/AO5z4tcW1CjA+r44sWLXWo8Wmyb0idgAyjFq8A3nj59ukuNQ0udrMzYAL/lfctj0iLf169fO/+2XLp06fTdyNeuXdu8efPmtE/CZ3Ly7xtPJawekYXzvb9YbH5AJ8SE3w+H8Ls5xtO+tn327Fn3nmzqvHYWM2DEiQmy7SC3M+6NGzd2R36E5z94NiQ4HAQLHSqBY6EBYJ8CFFobuaVCo4E/bu8wO92M/SwYDRsNk4WGjXfSpijJxztsgTghz/aOeXP79u1un4eGk+tYiMGLFy/uUmedxbdv37r/JShv7MGKKLUFNTq4evXqD53JGLTa1kLsPX78+LsOOxevkBtAH5ohdUI2f85YtMiHH4CNoS9fvuw+nQxMGCDpBg0fTw264NDxlAIZkKWVDx8+bG7durVLlWn1O5hjPO1jW/o62g424LjtC9fGIgaMGtmrk8FYuUYldWcR7A93lxp8EDiyCftpNBjAEyxqLOzd8lpRo4Ffvn//vrvTnNNguSQfNmKgpDjhMw352I0dfsPs1nnQ1xa06GDON0Ho089m5eK1NICeAwxkkZv2nbZkLhBLDBqIIbVzfPY3GOgbcjdf5wU6xJfx8ZaZL2bP7t27t0uVafE7z9zayaG2VRsL+C55Hj58uNuzPhYxYGS0zwxjDTixNfzz58+7oOFzsD8MPpiJsXdgmsli0zIEDQeNhezAZu/g1gaDDvQydEagBmYg/N15bWz0yZcbVOWgQ+cuXqgD0PJUCvLwqAK+UcO+/tPaFrTq4JAMtW3fANDHK/qwN3dck31jLKft46/M4sCYA4tW+TQY0mAMHjx40P3HV5mxRt8chxadDomnPjQQ0qxaXzxxo0Td7SAoR6vfnTdT2ZY8/rprYjFL0jVLM6A7VG3MfuG4MnSwP6UGhbtK3YnZIGQjmNbOPg18HxrQ2E6UZ0hrl5BA8hETLD95UoOmVCPLXbbtDBiI0umVfINz/IxEiX39p68taNHB2AyxLR0Z9MlrbWL1wYbNiNcxlqj38deaQcu+7CMfP6OC/6gMboSszAwwuDlJcah4qoGBk+iLJ56trJkdG+J3581UtgXstlq2jjMpNSJsR+xdvm3Dtttz8k2u7d3TLpVn20lkv612DLSaGL1aPXtkCw/fNPM2mhOH1oMHX9yn7rXycQ18GqTzGrx8pDkXewJlqlxP6pj3g7549HIii64NKo861dJqI+pg24IaHZCH6+yDr3uOFtsil5fVp8HbydOqQzFGnSz2vCGMKR95fJ9COfZc0rl+h+v5urXGE3CNPt9Ehr5yRE3d9/W7ucaTIM++tm1pw0RtnaZmcilrFYWjkdduNYGAAXPGPQZq9asA12Z1Zvf7YLd2Ucc7R5CvhpIe8CXV0+dLNZotUEYtkoPN6rxVPmu7UozQAKYaeTXCbDSiQvvZZ/PYzV7Px7YtK4WvW85GHo75evbpgHP65OmDsmuR/L4Otl42j93UHtp9fZ0zx4fUj7JrqalTqw/0QRm11MjHpjzSs8eWU9L7PvEk8FXlZZPcVgY2zq2BOnGux+rAl62txe8oY0229X6bO78Pzl0CP/FnK+xk8CzFEBGY1ucZJabZgzxD9bs25q6HucvHMg1fZDqmeGM5i6XC2sdhcqwxBiOe9iPiaTgRT9OxqN9hDIJgGnj2kIfc9fzSMXDlypXTL18EwSGJeAqWyCIHjMwufvr0qfi1/SAIDgszAzzwbR8kXyu0MdzxT/ngfrBuIp6CpbHYJemgjtDvCXPXQ9hpvazRthFPwVREPE3HLAaMQRAEQRAEx0oMGCuIO8FxCf2eMHc9hJ3WyxptG/EUTEXE03TEl16CIAiCIAiCIjFgDIIgCIIgCIrEgDEIgmp4h+qxfKszCMYm4ilYEqsdMPJzOzwXoI0Xqwf9oCv/Unph9ckPz3po+HS85ueOyJe6FmWXroMtOTamTXOy5aBBzMk7BrXyWV36jonOSsdqyqKO/HacfhpDdqg5317L+4Yth60P62dstR1uykZWP7ZT4/fiWso+FCV7eZSPLRcL2EV5hNf3mHEEtXUaatd9adF5qV85z3gS0llKbqvPvva4pb8s5cvVYQnxJFLtRJ9tdI5tQ1bJPxMzlgi8yieo12/utVSCVx6VXjOlVyfVvIqqdC3S9hVtfLa25LjOrX3tFZC/hj49ePRqqJq8JcaQz+pSr78S6FT6U5ml11qR3x5XefwHrpM7P3Ut4cuRPkvYV3PJ70rkbOSv5X0NmXKvOGuhTz5RspcHuaRv5ZWOBftS8rNf59boO0XtOa11EjV2LTGGfGD9w5Lycek4BfntcV2b/1CKJws6s+cJ6c/7RI5cvTwlv+urA/tT/tgK16ih1bbISh7fTvTVi7TOYb+u2UJtnaZmcinHUBQObA16zLTql4D2AQOlBoX8QxqC1LXYZxu5VKBrX21jCIfSg4XrU64akn0YQz6vI2yoc3x80MjlbEwZ3r7ktflTeYTXj60r8vjr+gbZ4vf3+ULJRlzbNu4+DVZnQ6m1bclelpSuvR5z/iF9SR8+Xcuh69Rq1z4OLR+Qr9Yvx4wnQX6uSx2s/ZC/71xLqV6WlEzW72rqwPGcfmsZy7Zej6JUL51n4Zi9bg21dZqaRS1Ja9qXjSni3DQ775jmLTDkC/YHPT9//rzTZ0rnz5492zx69OjUNrXLKSm2Abv5+vXrLrX5brlmbuBn2waoe+UV9WZJai5IX9evX+/+w+XLlzdv377tPvt32KJnjqd4+fJlZ18LS0y8C1dcuHChs11q2ce+3QEdbTunXWqz+fz58w/nkP/du3e71Pek5Aaun6Jko2vXrm3evHlz6tMvXrzoXtlmoY7sH5s+e1mID3RtuXTp0ulr16gPbSXnKialY/SFLrTc9urVq04/1kaHoqVOrXY9BC3yQalfOc94AmxM/osXL+72nNHaHtf2l31+V1OHOcYTlNqJUr0+fPiw2d4c7I6cQOyxf40sZsCIEQiC7SC3M9aNGzd2R36ERp98OEBNwARlrl69eqp3Ghb7nAYNF0EG5GEwQIOlgG2F4OO5HpFrMKcGuRhsMOCRbkjPxddo3GUXQeOee/E/jeLTp093qe/hGOdaqK/trNTBf/v2rfvvwU+IRXRkoSFmn/WXFptz3vbuPzng6bMRnQn+qs6Sevpy8H3OG5sWeyETWF/78uXL7tOme90cEEfUm5i6fft2tw8oEz1ogJCz+760+qClZNdD0SpfS78yZjzh19xQ+UEqDGmPa+vV53c1dZhjPPW1E6V6cY73UdLsXyOLGDASBKAAxCDeGSwyIPnfv3/fBUxLJxR8j/TJfwIKFEw0XHRGsg2NGLbhLnoIr1+/7gKUhotNdrZ3inPg48ePnWyakUI3dHDncfd8aIgv7rDHBL9Qp8QATTHNfvTGDaBsTmNt7+hLILefFRS1NkImsIMqz5zaD+qhgYB0xmfJj/4YLCpm+ExM2UEDg0i2fW7uxqRk16lQO9jXr4wdT8wO53QzpD2urVef37Uwp3haU1s+NosYMHIXY2e1WqDRpGHMzXwE7dAYlu6g1IDQcKphYSvdkVsYWGjDdgTvEqgd5JwH3BH7O/lUHNFw52YrStDAchcv1AH0LSHSKWFTOzNBQy170yEB8vT5D2k7G12DtRHlMxuHTFwbpnqsoNZeQgNwNp334MGD7r/H32yhS3TOzRmDZQbrY3TgrXUSQ+w6hKHyQa5fGTue8FnNDrNppY3zOZaipT3u6y9Lfje0TRiDfWwLtp0o1Ytlbh87pHOPIyydxSxJ1yxjlJjCadeMAoLAYonFw37buLDprrcWNY5PnjzZ7ZkPWlrxjcWQu+0x0CDByscg/9atW7vUCcjbN5OTamQ5zw766GBoWDVbUaKUh9lHzfiV/Eedox8MWfpsRMduZWHwiL9NQa29UjDjRCevMviceoaK45pNVL3RKXZjluXQDKlTjV0PxT46F75fGTuefEwwIwj4OccO1R7X9Jfe7/ZpEw5Ni2372olSvXgO2o9NSLN/lWwdZ1JqRNgas8u37Uh2e06+ifRnxTe7yGPPOzZaTYxe+/RFHou1Dd8Oq71m6VqPH5/8LAS298gfuFYtY+hh21h+963aVpksY8jHcfQIKbv4NPlT+qYMlSNkA4E8NfHozxOSr69OwHW8PD4tSjbiWlYW0jYvcC3v762k6puiz14pyJOSmf2ypbWf9C9bKa28tdTIBi11arFrH2PIZ0FWzrX4czme0ivX8/WSHURtPElmex3Skq2lTpCqVwrK9H5XUwfSc4ynUjvRVy/OU5prer3UUFunqZlcylpFYRDy2s07I8i42oY2OGsBHdTg9Wadns/2WAp73DZeKWqulQs67wc1jRuQt4aSbPgS+2z9rG5S/lgL59fQKp/22f1qPP2W0zn5U428LcfaQfu1T3nYfDnWnlbuHLY+dpPuW21ky0vVkeO1PpaDsmtJ2QtsvdiUJ+dzVq/ert7+pFvhvFpq6mTz2C1Xvz44t5ZWnbNxTHh9ahsrniw6ZuUGlZM6ZinVC3I6yNklVwdBeXOLJ1FqJ/rqhT05lrJrDZy7BH7iz1bYyeBZiiEiMK3Ps3RMswd5hup3bcxdD3OXj+UnLWsdCyxRsRy17+Mwa4zBiKf9iHgaTsTTdCzqdxiDIJgGnsviiwi5B+vXyJUrV5LPgwXBvkQ8BUtkkQNGZhc/fTr5TcBjCrggmBJmBvhiiH84fI3QxnDHP8UD+8FxEPEULI3FLkkHdYR+T5i7HsJO62WNto14CqYi4mk6Ykk6CIIgCIIgKDKLGcYgCIIgCIJjZQkzjLEkvXJCvyfMXQ9hp/WyRttGPAVTEfE0HbEkHQRBEARBEBSJAWMQBEEQBEFQJAaMQRBUc/fu3aP5GZAgGJuIp2BJLG7AyO8ust6vTY7466+/zjrwaBgk89zk5K0DVqekhZXb7g+mAd+xturzJZv3f//7327vGdi0tjEnHz82bH9LrfZ860ep306lDI7VyiJ0Xo1v0kYgh4XzJJctgx8YZt95x6qVp+/apbzYWseot8UeYzt2WnQuUn7X5+MeyrDxVLJZCq6dihfJZjcrp4c627wlHfi8OZRPZS0hnkRfO5HSuT3Otlr+mZgWEfSuR/uOTvt+S/teyDkyhZxcrxbypt6TmXsn6pJo0cMU1Mpn31Wq96HmIK/eiao44Z2oguPss2Xm4Fr+/aq153Ourqt3slrwL/kd16jxN/JRTu17aXVdWzbn2jSfkVWgsxrd9OHrm8PKI3vlKOVVmv9APu8HOiY9tjLknPOkVr4WnUPO7/p83OPjqWSzFLnY4xyVIShLsqWwZSBXSXaf18aLRfJZWfjs5R1Cn25Fq20h1U74GOGYr3dOD7XU1mlqJpeyVlFy5JzDcdwHytxAfrbzlLPFERUstnEh6EqNzVJo0cMU1MjnOxA1gin7sM/HCrb0DRv7+hrxVFmi5nzv77aulO3rTnkln9M5LXGkRt52BP460qeFc6jjPvgyc5DPylO6dikvn9kE+WQj8thjgE5Kg5MUtXWaijF0Tj7yp/yu5OMeaw9RslkO5PR5vBykS+W0tCm+bM71vgTs0yDLn1PSby0l3Vp8PWqunWonOKcv7evZSm2dpmYRS9JMJT9//rz7/Msvv3T/PbybU1P7ftm6NBXN9LPy2fOYYuY8m7bYaX97zC5LgP0sXr16dbq/ZunhvLh+/fpmGwibhw8fdmnpjf0Wqyc7PS9d8t/rK9if+/fv7z6dIH+/cOFC99/y9evX7vWZlkuXLg16l+vLly83jx492qXasUvYxMe2M9mlNpsPHz50PmfBp9ifgzZg20h376bF3/zykYfjr1+/3qXOQD/oSdilQXHz5s3Nixcvdqnx0DVtrF2+fHnz9u3bXeqMvrzYGLkF/kFdiefPnz//0B5S73fv3u1Sx0OLzqHkdyUf96TiqWSzFqwcQF9Tit2WNsWWjVy8mpd+10LfQD0uXry42/M9c4wnkWsnrl27tnnz5s3pYwbIb+v97Nmzzif62qE1sIgB48ePH3efNllHFDgKjkxgbwfEXWeEMXM8ffr09DjncQ4wQL19+/Z3aQUvjkEwc2x7B9Md08DPOhIdX6rhoNGWbDibHHEOID91oz6//fZbpx8LstK5IL90o8E1wWj1FYwLvr69G/6hk4CrV692/+0NyZcvX3af2qAzY7C5D7rJoOG1EAteftLsT4GfUYZiCF8lbetpYT/PiaUg/uyxVOeMHrnG2DBw9e0UOud9w56+vMhr20np99u3b12Hjb7UoUKq3sdAi85r/C7n455UPJVstg+0yQx4aim1KYL+L9WnoiP6Bj8ItcwxnqDUTjDopB9nfIB9sZ/0Q51tX8hxG1trY3XfktYMpAJShs11KBYN7uRovBjegzPQICiP7sQY+HlwLIJHziSePHnS/ddMXuo6U4G+GGxTnwcPHuz2noGsDAYJDDaCn3qCOiIGmb7OweHB1/0dvsCO+DN2lK34zE3QFCgO8C0a3qE3Sdw8EnuqN/Wkg0vNWqjhtrMMFmYT8F/pRzGdyr+WQRV2QF83btw4rTdxa2e3gh+p8btD+fihwGcZIOX8P0WpTRHEDfVEH/iPYOKg71wxp3jqaycEdgXbhmp8AeiFPOrX18giBoz2zqu0VFWCO0McWo0kW+uyad/dng8C60xL4t69e7tPaQgKGgxtNErUlf3qiGoG6MFwSnfEQh0Ym+7qUzcB5wk3E8zsabaTZSIfN6TZrxkbbTmfyg126PzsgJkbHQZHfBbWj5GLQcAU0Mb5mRd0lPpGZl9eOnK71C796uaWTl111k0yvnJstOg8Rc7vvI/X0GezIfjl6L54qmlTLJooQFbKVmyx0Q8A9Zpi4Nxi2752AvmZKLETIbnlZ/L4666JRQwYGfkTgJB7BqHPKemAbEPJVns3VMvQAeLSBpZ+uVADbwWUZramaCiOAem1ZeYAGxFDLeeI2g60FuvvLJf5ZSLS7LcDXjb8S0tafpCZmjnVTIg2BoPogM8edEonodn/80Z2sfUizm7durVLndGXF13YwQo3unTcqXZGj+8cIy06b/E7KLXpqXhqsVktzH7am/9UPIkhbYqVzZfNo1qAzqa4GWmxbV87wTK7rSuDR9qKHNhttWyVMiktIpCXbWvQ3Z4TSG8bve4z34oij9Jbw3fpreN26RRbA3d5tgOd79L6hhWfbVplgq4nmbiO8vtrar8vpyTbvlB+K6qD5LSgI45JV+Sl7vynPoJ9yjMHhujhPKmVD52m/L8EZVvbWIgT/L0E5eeuUXO+Rb5lQTb5CuXlZBUct3koL+WrHuqQKpv9lJGKQ+RqqV+KWttSd+lZ7UqOUl6vY+T3sahzKGcItXWailr5WnRe63cpH7dwPV1T1NjMg+w530SulK+nGNKmgNWdR7r0MTXXeLJwjtUd5dhzSed0y7kpn+ijVrapmVzKVkXJeHbzgUXaHk91BMKXh8FtGsfQZ+vofNZ+GzQ2v3cqzlHDoK0k2yHgGi2k9OGxeaQT6lGq+9Qg05ypkc/7pjb5v45jC+tnPj6EtZfOS0EDmGrkc+erccZPwOZJlQOKp9xxj722rZ/VgYdj1i9Vht3n4RzVYyhcoxZrY1uHVL1yeUE2YLPy27YxpaNaOH/OtMjXovOc32kfW58P5+IpZzMfT2DlYPO2RPZc3Fts3e2mc60OrO+wleJCMqfkmms8CY75NsGWY23ndVKj8xScuwR+4s9W2MngOYGJRVg1od8T5q6HucvHkjbPbB3Ts24sZ7FUmPtmZS1rjMGIp/2IeBpOxNN0xIBx5YR+T5i7HpZgJ5694gHxY+nksMmnT5/2eo4M1hiDEU/7E/E0jIin6Vjdz+oEQTAOzAzwwLd/8H+N0JnTgO/buQVBjoinYGnEDOPKCf2eMHc9hJ3WyxptG/EUTEXE03TMYsAYBEEQBEFwrMSAMQiCIAiCIFg4m83/A44d9lLMsHxTAAAAAElFTkSuQmCC\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Comparing the date of weight change between the initial and the respective decades, the higher the weight change was, the higher HR for diabetes and hypertension were calculated. Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. presents these ratios only for 10% and 30% increases in weight between decades. Early increase of body weight was related to higher HR for men for developing diabetes and for women for developing hypertension. In many cases, the increases were much higher than 30%, often exceeding 50%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Weight changes between decades and changes of hazard ratios\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;815\u003c/p\u003e\n \u003cp\u003echanges by decades\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e20-30y\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted \u003cstrong\u003eHR\u003c/strong\u003e (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted \u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (1.08\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (0.92\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23 (1.26\u0026ndash;3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89 (0.78\u0026ndash;4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (1.03\u0026ndash;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 (0.49\u0026ndash;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo-mor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (1.20\u0026ndash;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75 (1.10\u0026ndash;2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e30-40y\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (1.03\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.13\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55 (1.10\u0026ndash;2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48 (1.43\u0026ndash;4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (1.06\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 (0.47\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo-morb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (1.11\u0026ndash;1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 (0.98\u0026ndash;2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e40-50y\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.98\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.09\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (0.93\u0026ndash;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93 (1.28\u0026ndash;2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (1.07\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 (0.49\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo-morb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.12\u0026ndash;1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.02\u0026ndash;2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e50-60y\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.91\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.90\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.75\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (0.73\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (1.07\u0026ndash;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.55\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo-morb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (1.16\u0026ndash;1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (1.11\u0026ndash;2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e60-70y\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 (0.62\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.48\u0026ndash;1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 (0.24\u0026ndash;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.11\u0026ndash;5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.55\u0026ndash;2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.13\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo-morb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (1.00-4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDec-\u003c/strong\u003e life decades of patients \u003cstrong\u003e. y\u003c/strong\u003e -year. \u003cstrong\u003eHR\u003c/strong\u003e- hazard ratio of developing morbidity (diabetes or hypertension) adjusted for gender, age, and co-morbidity. \u003cstrong\u003ep\u003c/strong\u003e- level of significance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-morbidity\u003c/strong\u003e- \u003cstrong\u003eNo\u003c/strong\u003e: diabetes or hypertension only (*relevant for all decades)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u0026nbsp;\u003c/strong\u003eboth are present\u003c/p\u003e\n\u003cp\u003e*HR of male is relevant for all decades\u003c/p\u003e\n\u003cp\u003eAmong women, weight changes around delivery and menopause had also an impact on the development of diabetes and hypertension. The means of body weight, registered by the participants at the last delivery and at the age when menopause related symptoms occurred, correlated significantly with diabetes and/or hypertension resulting higher probabilities for developing the diseases (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). There were no significant differences between numbers of children and the weight gain of the mother.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e. Mean body weight at the age of 20y, after last delivery, in the last decade before and around menopause. Odds of diabetes and diabetes\u0026thinsp;+\u0026thinsp;hypertension (OR, 95% CI and p-value) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWOMEN\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;408\u003c/p\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean [kg]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDiabetes\u0026thinsp;+\u0026thinsp;Hypertension\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e95% CI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e95% CI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.95\u0026ndash;1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.96\u0026ndash;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elast delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01\u0026ndash;1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01\u0026ndash;1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elast decade before menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.91\u0026ndash;1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.91\u0026ndash;1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eat menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01\u0026ndash;1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01\u0026ndash;1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eMain findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly weight gain resulting overweight and obesity is significantly associated to the incidence of metabolic diseases. Patients with diabetes recorded the highest increase expressed in body weight, in BMI and in percent of the weight at 20y. Body weight and BMI changes over decades, was the highest in men with diabetes, higher among patients with hypertension and the less within control groups in both gender. Among patients with diabetes and hypertension, both diagnoses were set up earlier, than by patients having only one of them.\u003c/p\u003e\n\u003cp\u003eAlthough minimal differences were proven between surveys, the trend was clear, confirming the harmful effects of early weight gain in the younger decades, with small differences between genders. Significant\u0026nbsp;weight gain around the (last) delivery and menopause increase the chance for diabetes and/or hypertension as well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecade-long comparison of data could be mentioned as the real strength of this study.\u003c/p\u003e\n\u003cp\u003eThe most important methodological limitation was the reliability of data based on memory and personal records, which could err in both directions. The medical records used could not counterbalance this bias with certainty.\u0026nbsp;Unfortunately, regular body weight measurement is not part of the routine medical practice. People usually remember better at what age and what life event they started to gain more weight (e.g. marriage, divorcing, graduation, changing work places or accommodation, pregnancies, menopause, required changes of “wardrobe”, quitting smoking, getting ill or hospitalized, etc.).Although there could be some unreliability of the reported data, the trends could be clearly verified, in our national and in international studies as well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;There were only a few decades’ long studies, because most of the researchers are not able to conduct studies over decades, following the patients for a lifelong period, and correctly registering their anthropometric parameters. This is the reason, why other studies with exactly measured anthropometric data covered only years and never decades. The accuracy of data and longevity of follow-up need some compromise.\u003c/p\u003e\n\u003cp\u003eThere are other theoretical and practical limitations of our studies such as:\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp;therapy and interventions started at a younger age may have modified the findings, although there was not such a wide variety of drugs available for this generation, compared to more effective medications recently used,\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp;genetic elements and other risk factors remained undiscovered,\u003c/p\u003e\n\u003cp\u003e- patient’s inclusion was not standardized and those who are affected by metabolic diseases represent the largest proportion of persons attending\u0026nbsp;primary care surgeries, hence they were overrepresented. Their ratio in this study was much higher than in the general population, and could not be considered as representative, although both urban and rural inhabitants were involved,\u003c/p\u003e\n\u003cp\u003e- if data would be available, weight at birth, eating habits, socioeconomic circumstances, sleep duration, physical activity and their changes could be analyzed as well. More information about the time of menarche, on lactation, medication, surgical menopause or hormonal replacement therapy could also be useful, so\u0026nbsp;were unable to exclude all of the confounding factors.\u003c/p\u003e\n\u003cp\u003e- decade, as a threshold limit, does not always reflect biological or pathological changes properly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The same BMI does not mean the same body weight and height in different life periods. Due to the normal decrease in body height and \u003cem\u003esarcopenia\u003c/em\u003e, the same weight in the elderly means a higher BMI [6]. While we had data only of recent measurement and self-reports on heights at 20y, it may result some slight differences in BMI values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with previous studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing questionnaires has been an acceptable tool for data recording.\u0026nbsp;Epidemiological studies using similar method, questionnaire-reported and staff-measured weights were highly correlated.\u0026nbsp;In the famous “\u003cem\u003eThe Nurses’ Health Study I. and II.”\u003c/em\u003e and “\u003cem\u003eThe Health Professionals Follow-up Study\u003c/em\u003e” body weight was also questioned only, beside data on other lifestyle practices (nutritional habit, physical activity, sleep duration, “screen-watching time”) and medical history [3].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The similarities of weight gain between the control and hypertensive groups may be due to the high prevalence of hypertension among elderly. Steady weight or slow increases in body weight have been seen more often among people without diabetes. Perhaps due to the treatment, a slight body weight decrease was observed among diabetics after 60years.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;History of lifelong weight change may contribute to our understanding of why some obese older people are metabolically healthy but others are not [12].\u0026nbsp;Physical activity should also influence long-term weight gain, but evidence to support this expectation has been surprisingly inconsistent [13,14,15]. In addition, the duration of television viewing and of sleep may influence energy consumption, energy expenditure, or both [15,16]. In most of the epidemiological studies different lifestyle behaviours have often been evaluated separately,thus limiting relative comparisons or the quantification of combined effects. In addition, most studies of long-term weight gain have evaluated current behaviours, but changes in behaviour over time may be more relevant in terms of both their biologic effects on long-term weight gain and their translation into prevention strategies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The increasing prevalence of obesity has led to several attempts to counter this epidemic by various types of intervention. The most promising approaches are targeting children, because prevention programmes in early life are more likely to be successful and childhood obesity tracks into adulthood [17]. Outcomes of 71 European community-based initiatives against childhood obesity starting between 2005 and 2011 were critically evaluated. There were common characteristics: application of integrated actions at a local level, aimed at changing the environment and the children’s behaviour directly. Evidence supporting effectiveness on weight indicators is available, although the design and conduct of most of these studies were suboptimal (i.e. no control group, a small sample size, not random) [18].\u0026nbsp;Focusing for the whole family, not only for children, the \u003cem\u003eFeel4Diabetes\u003c/em\u003e project ran in primary schools and local communities, considering the socio-economic status of parents as well, discovered new patients with (pre)diabetes among them [19,20]. In the past decades, after realizing childhood obesity as a real public health threat, many public and governmental initiatives were started. Most of them presented good outcomes, but usually over short time period. Besides these programmes and studies, new governmental strategies were implemented in some affected countries [21,22]. Since 2013, in Hungarian primary schools a daily physical exercise hour is a part of the curriculum. It will hopefully increase the level of physical activity of the population and decrease the prevalence of obesity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Obesity management was in the focus of some interventions in the primary care. In a regional project in Hungary, most of the obese patients lost weight, laboratory parameters improved and blood pressure decreased [23]. In other, patients were faced to their own\u0026nbsp;cardiovascular risk assessment and this impression facilitated changes in health behavior and lifestyle [24].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sustained improvement in risk factor status requires ongoing risk communication with health care providers.Individualized lifestyle counselling improves health behaviour and reduces total cardiovascular risk among middle-aged men [25].\u0026nbsp;Primary care counselling for healthy young people driving their attention on current behaviors might elicit their proactive role to improve lifestyle, getting immediate advantages such as well-being improvement and the possibility to manage stress better [26]. Changing dietary habits is still the first step in the treatment of obesity with\u0026nbsp;a focus on components of the Mediterranean diet and to avoid a high intake of ultra-processed food because it has been related to an increased risk of obesity and obesity-associated comorbidities [27].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The simple obesity prevention differs from weight-loss trials. Weight-loss trials have typically enrolled obese or overweight persons who attempted substantial short-term weight loss on specialized diets, thus limiting the generalizability of the findings to non-obese populations and to the factors that determine long-term, gradual weight gain [3].\u0026nbsp;When obesity already developed,\u0026nbsp;utilization of counselling was lower and insufficient achieving 5% or greater weight loss at the population level [28].\u0026nbsp;In the UK, where more resources were available in primary care, the nationwide \u003cem\u003eCOUNTERWEIGHT\u003c/em\u003e project was longer and supported by other health care professionals and by networks of “Patients’ clubs” [29]. Appropriate financial incentives were implemented to motivate the primary care providers [30].\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe patient-doctor collaboration and confidence is the closest in primary care. Family physicians are in higly responsible position in all health care systems, they should not just be inactive observers of their patients’ weight gain; they must act in due time. Instead of restrospective analyses of mass-data, we have to act earlier at individual level. All patients with visible weight gain should be systematically and individually examined, exploring the reasons and determine the type of intervention. Applying financial incentives for appropriate obesity management could be a useful tool as well. Regular anthropometric measurement should be incorporated into routine medical practice. GPs should focus also on the younger generations, who rarely visit medical settings and should provide them advice for obesity management, if needed. Our retrospective findings can initiate interventions much earlier. Primary care capacity and workforce should be linked with the global need for prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe partners of the international study declared that no detailed approval was necessary according to their national regulations, since there were only anonymous data and there was no intervention or influence on the patient and the respective medical care process. In Hungary, ethical permission was issued by the \u003cem\u003eSouth- Budapest Ethical Committee\u003c/em\u003e (Jahn Ferenc Hospital, on 21 August 2000) and was updated by the \u003cem\u003eHungarian Committee on Scientific and Research Ethics\u0026nbsp;\u003c/em\u003e(ETT-TUKEB 20928-I). All of the patients signed the informed consent form linked to the data-sheet. The data were processed anonymously by the respective family physicians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData of all studies were deposited as electronic files at the Department of Family and Occupational Medicine, University of Debrecen. Hard copies of questionnaires were deposited also here and in the offices of the leading authors of the respective studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eIR- conceptualization, methodology, investigation, writing: ZJ, EK, PT, CM writing, organizing their own studies and searched literature, PT, KLR contributed in the validation, investigation and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the submitted version of the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere was no funding for these studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are thankful to the contributing family physicians for collecting and providing data. Special thanks to \u003cem\u003eTibor Deutsch, Endre Szigethy, Tímea Ungvári\u003c/em\u003e for the statistical analysis presented in the different surveys.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAstrup A. The Healthy lifestyles in Europe: prevention of obesity and type II diabetes by diet and physical activity. Public Health Nutrition 2001;4(2B):499-515. doi: 10.1079/phn2001136.\u003c/li\u003e\n \u003cli\u003eWadden TA, Tsai AG. Addressing Disparities in the Management of Obesity in Primary Care Settings.N Engl J Med. 2020;383(10):977-978. doi: 10.1056/NEJMe2025728\u003c/li\u003e\n \u003cli\u003eMozaffarian D, Hao T, Rimm EB Willett WC, Frank B, Hu N: Changes in Diet and Lifestyle and Long-Term Weight Gain in Women and Men. Engl J Med. 2011;364:2392-2404.\u003c/li\u003e\n \u003cli\u003ePatel SR, Hu FB: Short sleep duration and weight gain: a systematic review. 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World J Diabetes. 2011;2:8-15.\u003c/li\u003e\n \u003cli\u003eRurik I, Sandholzer H, Kalabay L: Does the dinamicity of weight gain predict the elements of metabolic syndrome? Med Sci Monit. 2009;15(2): CR40-44.\u003c/li\u003e\n \u003cli\u003eJancs\u0026oacute; Z, Halmy E, Rurik I: Differences in weight gain in hypertensive and diabetic elderly patients. Primary care study. J Nutr Health Aging. 2012;16: 592-6.\u003c/li\u003e\n \u003cli\u003eKov\u0026aacute;cs E, Jancs\u0026oacute; Z, M\u0026oacute;cz\u0026aacute;r Cs, Szigethy E, Frese T, Rurik I. Life-long Weight Change can Predict Metabolic Diseases. Exp Clin Endocrinol Diab. 2012;120: 573-8.\u003c/li\u003e\n \u003cli\u003eRurik I, M\u0026oacute;cz\u0026aacute;r Cs, Buono N, Frese T, Kolesnyk P, Mahlmeister J, Jancs\u0026oacute; Z. Early and menopausal weight gain and its relationship with the development of diabetes and hypertension. An international study on lifelong weight gain and manifestation of metabolic diseases. 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Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365:1876-85.\u003c/li\u003e\n \u003cli\u003ePigeot I, Baranowski T, De Henauw S. and the IDEFICS Intervention Study Group. The IDEFICS intervention trial to prevent childhood obesity: design and study methods. Obes Reviews. 2015;16 (Suppl. 2): 4\u0026ndash;15\u003c/li\u003e\n \u003cli\u003eBemelmans WJE, Wijnhoven TMA, Verschuuren M, Breda J. Overview of 71 European community-based initiatives against childhood obesity starting between 2005 and 2011: general characteristics and reported effects. BMC Public Health. 2014,14:758 http://www.biomedcentral.com/1471-2458/14/758\u003c/li\u003e\n \u003cli\u003eLatomme J, Huys N, Cardon G, Morgan PJ, Lateva M, Chakarova N, Kivel\u0026auml; J, Lindstr\u0026ouml;m J, Androutsos O, Gonz\u0026aacute;lez-Gil EM, De Miguel-Etayo P, N\u0026aacute;n\u0026aacute;si A, Kolozsv\u0026aacute;ri LR, Manios Y, De Craemer M; on behalf of the Feel4Diabetes-study group.Do physical activity and screen time mediate the association between European fathers\u0026apos; and their children\u0026apos;s weight status? Cross-sectional data from the Feel4Diabetes-study. Int J Behav Nutr Phys Act. 2019;16:100. doi: 10.1186/s12966-019-0864-8.\u003c/li\u003e\n \u003cli\u003eManios Y, Mavrogianni C, Lambrinou CP, Cardon G, Lindstr\u0026ouml;m J, Iotova V, Tankova T, Civeira F, Kivel\u0026auml; J, Jancs\u0026oacute; Z, Shadid S, Tsochev K, Gallego R, Rad\u0026oacute; S, Dafoulas G, Makrilakis K, Androutsos O, on behalf of the Feel4Diabetes-study group. 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Copenhagen: WHO Regional Office of Europe/HBSC Forum.)\u003c/li\u003e\n \u003cli\u003eM\u0026oacute;cz\u0026aacute;r C, Borgulya G, Kov\u0026aacute;cs E, Rurik I. Could primary care dietary intervention combined with lifestyle changes be effective in the cardiovascular prevention? Acta Alimentaria 2012;41:248\u0026ndash; 256. DOI: 10.1556/AAlim.41.2012.2.11\u003c/li\u003e\n \u003cli\u003eOcsovszky Z, Martos T, Otohal J, Ber\u0026eacute;nyi B, Merkely B, Csabai M, Bagyura Z. Relationship between cardiovascular risk assessment and health behavior in the light of psychosocial factors. Orv Hetil. 2023;164:119-131. doi: 10.1556/650.2023.32685. (Hungarian)\u003c/li\u003e\n \u003cli\u003eReijo Siren R, Eriksson JG, Vanhanen H.Observed changes in cardiovascular risk factors among high-risk middle-aged men who received lifestyle counselling: a 5-year follow-up.Scand J Prim Health Care. 2016;34:336-342. doi: 10.1080/02813432.2016.1248649.\u003c/li\u003e\n \u003cli\u003eLucini D, Pagani E, Capria F; Galiano M, Marchese M, Cribellati S, Parati G. Age Influences on Lifestyle and Stress Perception in theWorking Population. Nutrients. 2023,15,399. https://doi.org/10.3390/nu15020399\u003c/li\u003e\n \u003cli\u003eG\u0026oacute;mez-Ambrosi J. Recent Progress in the Management of Obesity. Nutrients. 2023,15,2651. ttps://doi.org/10.3390/ nu15122651\u003c/li\u003e\n \u003cli\u003eHenderson J, Ehlers AP, Lee JM, Kraftson AT, Piehl K, Richardson CR, Griauzde DH. Weight Loss Treatment and Longitudinal Weight Change Among Primary Care Patients With Obesity. JAMA Netw Open. 2024 Feb 5;7(2):e2356183. doi: 10.1001/jamanetworkopen.2023.56183.\u003c/li\u003e\n \u003cli\u003eCounterweight Project Team, McQuigg M, Brown JE, Broom JI, Laws RA, Reckless JP, Noble PA, Kumar S, McCombie EL, Lean ME, Lyons GF, Mongia S, Frost GS, Quinn MF, Barth JH, Haynes SM, Finer N, Haslam DW, Ross HM, Hole DJ, Radziwonik S. Engaging patients, clinicians and health funders in weight management: the Counterweight Programme. Fam Pract. 2008;25-S1:i79-86. doi: 10.1093/fampra/cmn081\u003c/li\u003e\n \u003cli\u003eNHS-QOF. Available at https://www.england.nhs.uk/wp-content/uploads/2021/03/B0456-update-on-quality-outcomes-framework-changes-for-21-22-.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Semmelweis University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"diabetes, family medicine, hypertension, metabolic diseases, obesity, prevention, primary care","lastPublishedDoi":"10.21203/rs.3.rs-4426620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4426620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObesity management is a big challenge for health care providers. Primary care is the appropriate level, not only for the management but for the prevention and early recognition as well. Obesity develops gradually and needs attention in the early phase of weight gain.\u003c/p\u003e \u003cp\u003eThe main results of four Hungarian and international studies in primary care settings were summarized, seeking relationship between weight gain in younger life and development of metabolic diseases.\u003c/p\u003e \u003cp\u003eData of primary care patients were collected about the changes of their weight gain from 20y to the present. Source: medical files and self-reports.\u003c/p\u003e \u003cp\u003eEarly weight-gain between 20y and 30y means a serious risk for developing diabetes, between 30y and 40y for hypertension and even faster weight-gain could be a risk factor for both metabolic diseases. In females, significant weight gain around pregnancies and the menopause could increase the risk of these morbidities as well.\u003c/p\u003e \u003cp\u003ePrimary care service providers/family physicians/general practitioners ought to be not only an inactive observers, they have to give more focus on those of their patients who show conspicuous weigh gain in their younger decades, to explore the individual reasons and to initiate the appropriate intervention as early as possible.\u003c/p\u003e","manuscriptTitle":"Early Obesity Counselling in Primary Care Setting Could Decrease Metabolic Diseases Are Diabetes and Hypertension Avoidable, if Stopping Early Weight Gain?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 01:43:04","doi":"10.21203/rs.3.rs-4426620/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96834f4c-1c1d-42aa-ae53-1c34092b679d","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-23T01:43:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 01:43:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4426620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4426620","identity":"rs-4426620","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}