Analysis of Body Mass Index, the Mandible, and Dental Alveolar Arch Factors in Prediction of Mandibular Third Molar Impaction: A Pilot Study

2010 ◽  
Vol 11 (6) ◽  
pp. 41-48 ◽  
Author(s):  
Babatunde O. Akinbami ◽  
Blessing C. Didia
Keyword(s):  
Body Mass Index ◽  
Pilot Study ◽  
Body Mass ◽  
Third Molar ◽  
The Body ◽  
Third Molars ◽  
Determinant Factors ◽  
Mandibular Third Molar ◽  
The Difference ◽  
Alveolar Arch

Abstract Aim The aim of this study was to determine how some physical characteristics can be used to predict the occurrence of impacted mandibular third molars. Background While the concept of prophylactic removal of the asymptomatic erupting or impacted mandibular third molar has generated much controversy over the years, new theories of therapeutic surgical removal of the erupting tooth and therapeutic agenesis of the tooth bud are emerging. However, there are a few studies that address the anthropometric factors that could predict an impacted mandibular third molar. Methods and Materials The study included Nigerian patients of both genders who were at least 16 years of age. A total of 83 subjects participated in the study; there were 44 (53 percent) females and 39 (47 percent) males. The subjects were divided into two categories presence of impaction (Group 1) and absence of impaction (Group 2). Impaction of the mandibular third molar was assessed by clinical and radiographic evaluation. Body mass index (BMI) of each subject was determined by measuring the body weight (BW) and body height (BH), then dividing the weight of the body by the square of the height. The mandibular index (MI) was assessed by measuring the length and width of the mandible (MW). It was calculated by dividing the width of the mandible by the length of the mandible. The mandibular length (ML) consisted of the total teeth sizes of the three anterior teeth, the two premolars, and the first and second molars. These dimensions were measured with a divider/ ruler and recorded. The anterior-posterior distance of the arch from the midline to the retromolar pad (alveolar arch length) also was measured. Results Eighty-one (97.6 percent) of the participants were between 16 and 23 years old, while 2 (2.4 percent) were between 30 and 39 years old, of which 44 (53 percent) were women and 39 (47 percent) were men. There were 38 (45.8 percent) cases of impaction and 45 (54.2 percent) cases of unimpacted third molar. The mean and standard deviation values of BMI for the two groups in males and females were 21.10±1.90, 22.40±2.70 and 22.00±2.40, 22.30±1.99 respectively, with no significant difference, p>0.05, CI 95%. The two determinant factors of impaction were mandibular length and the difference between alveolar arch length (p=0.04) and total teeth size. Both of these variables had significant inverse correlations with impaction values of p=0.04 and p=0.003, respectively. The prediction values were 59 percent for mandibular length and 81.9 percent for differences between mandibular length and teeth sizes, respectively. The synthesized prediction value by the two determinant factors is 75.6 percent. Conclusion The prediction of mandibular third molar impaction was mainly dependent on two factors: the length of the mandible and the difference between arch length and total teeth size. Clinical Significance Small mandible, small dental arch, and large teeth are risk factors that are strongly associated with the occurrence of impacted third molars. Citation Akinbami BO, Didia BC. Analysis of Body Mass Index, the Mandible, and Dental Alveolar Arch Factors in Prediction of Mandibular Third Molar Impaction: A Pilot Study. J Contemp Dent Pract [Internet]. 2010 December; 11(6):041- 048. Available from: http://www.thejcdp.com/ journal/view/volume11-issue6-akinbami

scholarly journals Intranasal insulin influences the olfactory performance of patients with smell loss, dependent on the body mass index: A pilot study

2015 ◽  
Vol 53 (4) ◽  
pp. 371-378 ◽  
Author(s):  
V. Schopf ◽  
K. Kollndorfer ◽  
M. Pollak ◽  
C.A. Mueller ◽  
J. Freiherr
Keyword(s):  
Body Mass Index ◽  
Pilot Study ◽  
Body Mass ◽  
Memory Function ◽  
Insulin Level ◽  
Type Ii Diabetes Mellitus ◽  
The Body ◽  
Intranasal Insulin ◽  
Healthy Humans ◽  
Olfactory Loss

Background: The application of intranasal insulin in healthy humans has been linked to improved memory function, reduced food intake, and increased olfactory thresholds. There has also been some correlation between the morbidities associated with central nervous system (CNS) insulin resistance, such as type II diabetes mellitus, Alzheimer's disease, obesity, and impaired odour recognition. Given that impaired odour recognition is an important component of olfactory performance, mechanisms that govern these effects may account for impaired olfactory functions in anosmic patients. Methodology: Ten patients with post-infectious olfactory loss received intranasal administration of 40 IU insulin or a placebo solution, as well as olfactory performance tests before and after administration. Results: When administered insulin, patients exhibited an immediate performance improvement with regard to olfactory sensitivity and olfactory intensity ratings. In addition, more odours were correctly identified. Furthermore, an improvement in the odour identification task was detected in patients with higher body mass index. Conclusion: Results of this pilot study shed light on the link between cerebral insulin level and an impaired sense of smell. This research line might provide a better understanding of olfactory loss in relation to eating and dietary behavior, and could offer opportunities to develop faster therapeutic intervention for patients with olfactory dysfunction.

scholarly journals Ostomy creation with fewer sutures using tissue adhesives (cyanoacrylates) in inflammatory bowel disease: a pilot study

2018 ◽  
Vol 100 (3) ◽  
pp. 190-193
Author(s):  
M Uchino ◽  
H Ikeuchi ◽  
T Bando ◽  
H Sasaki ◽  
T Chohno ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Inflammatory Bowel Disease ◽  
Pilot Study ◽  
Adverse Events ◽  
Body Mass ◽  
Bowel Disease ◽  
The Body ◽  
Fistula Formation ◽  
Tissue Adhesives ◽  
Inflammatory Bowel

Introduction Fistula formation around the ostomy site is a stoma-related complication often requiring surgical intervention. This complication may be caused by sutures or may develop as a complication of inflammatory bowel disease. Before conducting a clinical trial, we set out to investigate the safety of ostomy creation with fewer sutures using tissue adhesives in this pilot study. Methods Patients with inflammatory bowel disease who required surgery with ostomy creation at the Hyogo College of Medicine between January 2014 and December 2015 were enrolled. Safety was assessed by evaluating the incidence of stoma-related complications. Ostomy was restricted to loop ileostomy and was created with two sutures and tissue adhesives. Results A total of 14 patients were enrolled. Mean body mass index was 18.9 ± 2.0 kg/m2. There were no cases of ostomy retraction and no severe adverse events were observed. Conclusions This pilot study demonstrates that ostomy creation using tissue adhesives is safe. Although retraction and adverse events were not observed, even in patients with inflammatory bowel disease who generally exhibit delayed wound healing, the body mass index was extremely low in this series. This study does not strongly recommend ostomy creation with tissue adhesives; further studies are needed to clarify the efficacy and safety of the procedure.

scholarly journals Estimating the gains of early detection of hypertension over the marginal patient

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254260
Author(s):  
Paul Rodríguez-Lesmes
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Health Outcomes ◽  
Body Mass ◽  
Family Doctor ◽  
The Body ◽  
P Value ◽  
Suspected Case ◽  
Potential Impact ◽  
The Difference

This study estimated the potential impact of early diagnosis programs on health outcomes in England. Specifically, if advising individuals to visit their family doctor due to a suspected case of mild hypertension would result in (i) an increase in the diagnosis and treatment of high blood pressure; (ii) an improved lifestyle reflected in objective measures such as the body-mass-index and blood pressure levels; (iii) a reduced probability of the onset of other cardiovascular diseases, such as diabetes. To address potential selection bias in screening, a feature of the English Longitudinal Study of Ageing is exploited, motivating a regression discontinuity design. If respondents’ blood pressure measurements are above a standard clinical threshold, they are advised to visit their family doctor to confirm hypertension. Two years after the protocol, there is evidence of an increase in diagnosis (5.7 pp, p-val = 0.06) and medication use (6 pp, p-val = 0.007) for treating the condition. However, four years after the protocol, the difference in diagnosis and medication disappeared (4 pp, p-val = 0.384; 3.4 pp, p-val = 0.261). Moreover, there are no differences on observed blood pressure levels (systolic 0.026 mmHg, p-val = 0.815; diastolic -0.336 mmHg, p-val = 0.765), or Body-Mass-Index ((0.771, p-val = 0.154)). There are also no differences on diagnosis of diabetes (1.7 pp, p-val = 0.343) or heart related conditions (3.6 pp, p-value = 0.161). In conclusion, the nudge produces an earlier diagnosis of around two years, but there are no perceivable gains in health outcomes after four years.

scholarly journals Grip Strength Generalization of the Body Mass Index

2020 ◽  
Author(s):  
Alexandru Godescu
Keyword(s):  
Body Mass Index ◽  
Body Weight ◽  
Grip Strength ◽  
19Th Century ◽  
Body Mass ◽  
Fat Body ◽  
The Body ◽  
Fat Percentage ◽  
The Difference ◽  
The 19Th Century

The Body Mass Index (BMI) formula has been developed by Belgian mathematician Adolphe Quetelet and published in 1840 [1] as a law of nature and society, based on statistics about the weight and height of the population of that time, the first part of the 19th century. He called it “social physics”. From then, for nearly two centuries, the BMI had been the most important formula describing the normal relations and ratio of weight to the square of the height for humans. The problem arises if the BMI formula, developed in the first part of the 19th century is still good today when the type of work people perform is very different? In modern times, most people are less muscular than at the time when the BMI was developed because they do not work physically as heavy as at that time. In many cases, the Body Mass index can predict mortality, morbidity and illness but not always, for example cases such as (a) the obesity paradox for some cardiovascular problems and (b) the U shape mortality paradox as well as (c) false positive obesity diagnostic in regard to people who are strong and muscular, have low body fat percentage but are classified as obese by the BMI and (d) cases where BMI is normal but people have an “obese metabolism” (e) BMI normal but high fat percentage. The objective is to develop a formula good for all body types, a formula that makes the difference between fat and non-fat body weight such as muscle and body frame and quantifies the effect of strength and fitness, which BMI does not. Another objective is to develop a formula to predict the health risks and fitness status of people, better than BMI. The first generalizations of BMI using anthropometric metrics could be found in [2], where I discuss and analyze many formulae, developed, tested, and simulated by me, using similar new methods, accounting for body shape, physical shape and body function, making the difference between muscle mass and fat, fat and non fat body weight. Nearly all formulae and methods developed and proposed in this new model are new, never published before. Many experiments published before, in highly cited papers show that grip strength and muscle strength is a predictor of health, mortality, morbidity, endocrine and metabolic disease outside the BMI and anthropometric measures. The purpose of my formula is to explain the outcome of those experiments and create a formula which predicts these experiments [21-41].

scholarly journals Elevated body mass index and fatty liver

2008 ◽  
Vol 136 (3-4) ◽  
pp. 122-125 ◽  
Author(s):  
Dragana Marovic
Keyword(s):  
Body Mass Index ◽  
Fatty Liver ◽  
Body Mass ◽  
Overweight And Obesity ◽  
The Body ◽  
Average Level ◽  
Nonalcoholic Fatty Liver ◽  
Elevated Body Mass Index ◽  
Significant Difference ◽  
The Difference

Introduction Obesity and overweight, expressed by elevated Body Mass Index (BMI), result from excessive consumption of fatty food and carbohydrates above the body needs. The fat from the blood, through free fatty acids, is taken directly into the liver. Objective The aim of this study was to examine correlation among the accepted ultrasonography findings of the fatty liver and the normal ultrasonography findings and the elevated average level of BMI and those with normal BMI in examinees in one investigation. All was done aimed at proving that the BMI is one of the direct factors of the increased occurence of fatty liver. METHOD The method of the investigation consisted of anthropometric measuring of height and weight on the basis of which there were established BMI values. Consequently, the examinees were divided in two groups: one with normal BMI (under 24.9 kg/m2) and the other with increased BMI (over 25 kg/m2). Fatty liver was diagnosed when the liver of the examinees was observed by ultrasonography. Thus there were given subgroups of the examinees, one with the findings of fatty liver and the second with a normal finding, without changes. After that, the obtained results were statistically analysed. Results It was found that the average level of BMI in the examinees was by two units higher in the subgroup with ultrasonography findings of fatty liver than the average value of BMI in the subgroup with the normal ultrasonography findings of the liver. The difference was tested by the Student's t-test and a significant difference was found. The difference in frequencies of the appearance of the finding of fatty liver in the subgroups was tested by ?2-test. A statistically significant difference was found in frequencies of the appearance of fatty liver in the subgroup with the increased value of BMI. Conclusion The increased BMI, which is represented by overweight and obesity, is one of the direct risk factors which cause fatty liver, checked by the US findings. Fatty liver can later progress to nonalcoholic fatty liver disease (NAFLD). .

scholarly journals “Sacbe”, a Comprehensive Intervention to Decrease Body Mass Index in Children with Adiposity: A Pilot Study

2018 ◽  
Vol 15 (9) ◽  
pp. 2010 ◽  
Author(s):  
Ana Rodriguez-Ventura ◽  
Arturo Parra-Solano ◽  
Daniel Illescas-Zárate ◽  
Minerva Hernández-Flores ◽  
Carolina Paredes ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Pilot Study ◽  
Body Mass ◽  
Healthy Lifestyle ◽  
Complex Interventions ◽  
Active Participation ◽  
The Body ◽  
Sociocultural Context ◽  
Z Score ◽  
Main Challenge

Interventions in children with adiposity decrease less than 0.2 the body mass index (BMI) z-score less than 0.2 and only in 21–23% of cases. Experts recommend focusing on the habits of a healthy lifestyle (HLS) but considering the sociocultural context of children and their parents. Our objective was to achieve a higher percentage of success in lowering the BMI z-score in children with adiposity and their parents through a pilot program “Sacbe” based on HLS, sensitive to the sociocultural context previously explored and with the active participation of parents. This is a pilot study in children aged 8 to 18 years with adiposity according to the BMI z-score. The program consisted of two workshops on HLS and nutrition given by the pediatric endocrinologist in group sessions with 3–5 families and reinforcements in each visit by registered dietitians. We recorded lifestyle habits and anthropometric characteristics of children and their parents at the baseline visit and every month for 3–4 months. Forty-nine families, 55 children and 64 parents participated, 60% of the children were female, the average age was 13.95 ± 3.3 years, 72.7% and 86.7% lowered the z score of the BMI due to intention to treat and protocol analysis (p < 0.001), respectively; BMI z-score decreased by 0.22 ± 0.21, from 2.13 ± 0.57 to 1.91 ± 0.58 (p < 0.001). In total, 83% of the parents involved were mothers, the average age was 45.8 ± 9.4 years, 77% lost weight and body fat (p < 0.001), the frequency of unhealthy habits decreased. The results of “Sacbe” exceeded expectations by combining the active participation of parents, sessions in groups, and the education on various components of an HLS inside sociocultural context. The main challenge will be to standardize and reproduce this type of complex interventions, as well as to assure long-term success.

scholarly journals Protein-energetic malnutrition as a predictor of mortality in patients on haemodialysis

2009 ◽  
Vol 62 (11-12) ◽  
pp. 573-577 ◽  
Author(s):  
Radojica Stolic ◽  
Goran Trajkovic
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
The Body ◽  
Lower Survival Rate ◽  
Reactive Protein ◽  
Results Of Treatment ◽  
Outpatient Program ◽  
Significant Difference ◽  
Clinical Centre ◽  
The Difference

Introduction. Malnutrition in patients on haemodialysis represents an important factor of mortality. The aim of the study was to estimate the influence and predictive significance of malnutrition on mortality of patients on haemodialysis. Material and methods. There were analyzed the outcomes of treatment of patients on outpatient program of haemodialysis who were hospitalized in Clinical Centre 'Kragujevac' for different reasons. The study included the analysis of demographic characteristics, duration of dialysis, body mass index, indications for hospitalization, result of the treatment and biochemical parameters. Results. 39.2% of the examined patients had the elements of protein-energetic malnutrition; 36.8% of patients with malnutrition died; the difference between the groups was statistically significant (p=0.0006) regarding the results of treatment. Statistically significant difference(p<0.0001) was also obtained concerning the body mass index with its value of 17.1?1.55 kg/m2 in patients with malnutrition. Cardiovascular diseases were statistically more common in patients with malnutrition (p=0.037). In correlation of the group of patients with and the group without malnutrition a statistically significant difference in number of erythrocytes was found (2.87?0.71 vs. 3.26?0,5 x 1012; p=0.04), concentration of hemoglobin (85?15.7 vs. 104?15.7; p<0.0001), level of creatinin (874?229.3 vs. 998?237.8 ?mol/L; p=0.04), total proteins (66.5?5.4 vs. 70?4.47 g/L; p=0.001), albumin (30?3.7 vs. 38?4.38; p<0.0001), total cholesterol (3.05?1.14 vs. 4.31?1.2 mmol/L; p<0.0001), C-reactive protein (9.5?6.8 vs. 2.9?5.09 mg/L; p<0.0001) and concentration of fibrinogen (4.96?0.91 vs. 4.22?0.91 ?mol/L p=0.001). Survival time in patients with malnutrition was statistically shorter - 18 months (p<0.0001). Conclusions. A third of the examined patients in our study were malnourished with lower survival rate. More than two thirds of patients with malnutrition died.

scholarly journals The Anthropometric Generalization of the Body Mass Index

2020 ◽  
Author(s):  
alexandru godescu
Keyword(s):  
Body Mass Index ◽  
Body Composition ◽  
Body Mass ◽  
Body Fat ◽  
The Body ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
The Difference ◽  
The 19Th Century ◽  
Highly Cited

The classic Body Mass Index, (BMI), developed in the 19th century by the Belgian mathematician Adolphe Quetelet [1] is an important indicator of the risk of death, of obesity, of negative health consequences, body fat percentage and of the shape of the body. While he BMI is assumed to indicate obesity in sedentary people and in people who do not practice sports, it is undisputed and a consensus among researchers [2][3][4][5][9][25] that Body Mass Index (BMI) is not a good indicator for obesity in people who developed their body through heavy physical work or sport but also in other segments of population such as those who appear to have a normal weight but in fact have a high body fat percentage and obese methabolism. The BMI also does not include all the variables essential for a health predictor. The BMI is not always a good predictor of metabolic disease, people who appear of healthy weight according to BMI have in some cases an obese metabolic syndrome. The BMI was developed as a law of natural sciences and &ldquo;social physics&rdquo; [1], as it was called then, before the middle of the 19th century, and it had been used from the 70s for medical purposes, to detect obesity and the risk of mortality [6][7]. The BMI has a huge importance for modern society, affected by an obesity epidemic [8]. BMI has applications in medicine, sport medicine, sport, fitness, bodybuilding, insurance, nutrition, pharmacology. The main limitation of the BMI is that it does not account for body composition including non fat body mass such as muscles, joints, body frame and makes no difference between fat and non fat components of the body weight. The body composition and the proportion of fat and muscles make a difference in health outcomes [12][13][14][25][26][27][35][36][37] [38][39][40][41][42][43][44]&hellip;[100]. Body composition makes a difference also in the level of sport performance for athletes of every level. In nearly two centuries since the Body Mass Index was developed, no formula had been successfully developed to account for body composition and make the difference between muscle and fat in a consistent way. This can be considered a longstanding open problem of major importance for society. The objective of this analysis is to develop new formulae taking into account the health implication of body composition measured through indirect, simple indicators and making the difference between muscles and fat, healthy and non healthy metabolism. The formulae developed in this article are the only formula to successfully generalize BMI and make this difference. I develop a direct generalization of BMI, in the mathematical and physiological sense to account for fat and fat free mass and muscles, small and large body frames. It is the first such generalization because the classic BMI can be determined as a particular case of my formulae in the strict mathematical and practical physiologic sense. No other formula generalized the BMI to make the difference between fat and a large frame and muscles has ever been published in nearly two centuries since the BMI formula had been developed. The formulae I developed explain and generalize the conclusions of a large number of highly cited empirical experiments cited in the reference section. [35][36][37][38][38][39] [40][42][43][44]..[100] Most of the experimental proof I bring in support of my formulae and bodyweight quantification theory comes from many highly cited experimental research publications in medicine, sports medicine, sport science and physiology. My formulae explain also performance in decades of competitive sports and athletics

scholarly journals Intranasal insulin influences the olfactory performance of patients with smell loss, dependent on the body mass index: A pilot study

2015 ◽  
Vol 53 (4) ◽  
pp. 371-378 ◽  
Author(s):  
V. Schopf ◽  
K. Kollndorfer ◽  
M. Pollak ◽  
C.A. Mueller ◽  
J. Freiher
Keyword(s):  
Body Mass Index ◽  
Pilot Study ◽  
Body Mass ◽  
The Body ◽  
Intranasal Insulin ◽  
Smell Loss
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