ライフサイエンスコーパス: anthropometric

1 metabolic risk, inflammation, nutrition, and anthropometrics.

2 insulin, and HOMA-IR, independent of simple anthropometrics.

3 A research nurse obtained weekly anthropometrics.

4 rates of change of PDFF (DeltaPDFF) and body anthropometrics.

5 After anthropometric adjustment, bone deficits persisted in CI

6 Demographic, anthropometric, admission, illness severity, laboratory,

7 red with nonfood images) was correlated with anthropometric and behavioral variables.

8 rmore, statistically relevant differences in anthropometric and biochemical markers depending on sex

9 Anthropometric and biochemical parameters were recorded.

10 fter adjustments for age, smoking, drinking, anthropometric and biochemical variables, or menopausal

11 Anthropometric and bioelectrical impedance measures were

12 Childhood anthropometric and bioelectrical impedance outcomes incl

13 frequency questionnaires and fasting blood, anthropometric and blood pressure measurements were obta

14 in the general population across 12 complex anthropometric and cardiometabolic phenotypes (n = 2,231

15 which biological and environmental samples, anthropometric and clinical measurements, and additional

16 community-based nutritional intervention on anthropometric and clinical outcomes of children of wome

17 r-metabolic, neuropsychiatric, physiological-anthropometric and cognitive traits in the participants

18 6 in mid-childhood (median, 7.7 years) using anthropometric and dual X-ray absorptiometry (DXA) measu

19 mediating effects on the association between anthropometric and lifestyle factors and major BCL subty

20 ly lower" trajectory, accounting for age and anthropometric and lifestyle factors.Within both sexes,

21 obank, and its joint measurement of genetic, anthropometric and lifestyle variables, offers an unprec

22 he effect of olanzapine on the daily rate of anthropometric and metabolic measures significantly diff

23 lated soluble fiber supplementation improves anthropometric and metabolic outcomes in overweight and

24 Participants had anthropometric and nutritional assessments and seven sch

25 Study of Health in Pomerania, we determined anthropometric and periodontal parameters, C-reactive pr

26 examined relations between HMO and maternal anthropometric and reproductive indexes and indirectly e

27 termine the threshold effect between various anthropometric and the risk of localized Stage II/III pe

28 We studied body anthropometrics and arterial morphology and physiology i

29 Anthropometrics and biomarkers of glucose metabolism wer

30 ask, Baby Eating Behavior Questionnaire, and anthropometrics and demographics assessments.

31 study was to assess the impact of AMY1 CN on anthropometrics and glycemic outcomes in obese individua

32 We measured infants' anthropometrics and used dual-energy X-ray absorptiometr

33 Detailed and standardized sociodemographic, anthropometric, and clinical measurements were made.

34 Demographic, anthropometric, and diagnostic information were collecte

35 VD prevalence by levels of sociodemographic, anthropometric, and geographic factors using adjusted pr

36 yslipidemia in excess of measured lifestyle, anthropometric, and inherited genetic factors.

37 n-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were a

38 Clinical, biochemical, anthropometric, and questionnaire assessments were under

39 Clinical, anthropometric, and survival differences were small amon

40 Demographic, clinical, anthropometric, and treatment variables were examined as

41 Initial PDFF (PDFF(0)), initial anthropometrics, and anthropometric rates of change were

42 adjust for confounding factors such as age, anthropometrics, and smoking status.

43 ONTROL; matched for dose with LOW), on child anthropometrics, and to explore putative mediators of we

44 Anthropometrics applied to hand roentgenograms showed th

45 Among older adults, the optimal anthropometric approach to risk stratification of AF rem

46 Manual anthropometrics are used extensively in medical practice

47 Our results identified anthropometric arm indicators and MUAC/A measurements as

48 m circumference (MUAC) has long been used in anthropometric assessments of nutritional status in fiel

49 length-for-age z scores (LAZs) obtained from anthropometric assessments that incorporated covariate p

50 A range of prespecified, secondary anthropometric, behavioural, and psychosocial outcomes w

51 Anthropometrics, BMC, and body composition via dual-ener

52 Anthropometric, body composition, and behavioral data we

53 at birth, 1 and 3 months of age in terms of anthropometrics, body composition All the results were a

54 family sociodemographics, parental and birth anthropometrics, breastfeeding status, physical activity

55 Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and ex

56 urther explore physiological processes at an anthropometric, cellular, and molecular level.

57 Subjects were matched for other anthropometric characteristics and were studied using a

58 atus, nusinersen treatment, demographic, and anthropometric characteristics determine energy requirem

59 Growth measures were linked with age-5-years anthropometric characteristics using linear regression.

60 d whether birth certificate-derived maternal anthropometric characteristics were associated with incr

61 with respect to phenotypic traits related to anthropometric characteristics, dietary habits, social s

62 st associations of metabolomic profiles with anthropometric, clinical and biochemical parameters.

63 viously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular facto

64 etic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric tr

65 en-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for

66 No body anthropometric correlated with either outcome.

67 Socio-demographic and anthropometric data [anthropometric measurements, blood

68 substantial heterogeneity in the quality of anthropometric data across surveys.

69 Children were followed for 2.5 years and anthropometric data and medical information on infection

70 Clinical and anthropometric data as well as plasma Hcy level and glyc

71 cohort 1; n = 244 and cohort 2; n = 60) with anthropometric data at 10-12 weeks and plasma/serum samp

72 Anthropometric data collected at scheduled infant welfar

73 We combine anthropometric data for 192,000 children from 30 countri

74 We used anthropometric data for children 0-59 mo of age from all

75 esity in mid-life using objectively measured anthropometric data from UK Biobank.

76 Anthropometric data of the children were collected repea

77 We derived 6 indicators of anthropometric data quality at the survey level, includi

78 is was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately.

79 We aimed to develop composite indices of anthropometric data quality for use in multisurvey analy

80 ces can be used to account for variations in anthropometric data quality in multisurvey epidemiologic

81 recent DHS suggest potential improvements in anthropometric data quality over time, there continues t

82 Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys us

83 A composite index of anthropometric data quality using a parsimonious set of

84 However, the quality of the anthropometric data varies between surveys, which may af

85 Anthropometric data were collected using international p

86 Clinical and anthropometric data were collected, and biochemical and

87 Anthropometric data were complete at the 24-month visit

88 erviewed using a standardised questionnaire, anthropometric data were recorded, and an abdominal ultr

89 Dual-energy x-ray absorptiometry and anthropometric data were used to determine changes in bo

90 Participants' anthropometric data, blood values and objective PA were

91 lings and also after adjustment for maternal anthropometric data.

92 This finding was also independent of anthropometric data.

93 with dual-energy x-ray absorptiometry (DXA), anthropometric, demographic, and prescription medication

94 plementation on moderately preterm newborns' anthropometric development (weight-for-age and height-fo

95 Body anthropometrics did not predict either outcome.

96 Anthropometric, dietary, and physical activity measureme

97 , -11) mL after adjustment for demographics, anthropometrics, dietary factors, and smoking.

98 the present study were therefore to compare anthropometric dimensions, blood values, psychological f

99 A set of anthropometric, dry-land strength, thrust and speed vari

100 onary variables, exercise intensity and some anthropometric elements during aerobic exercise.

101 gitudinal Study from whom DNA and dental and anthropometric endpoints were collected during multiple

102 Measurement techniques and anthropometric equipment used in panel site clinics shou

103 y (Porto, Portugal; 2005-2017) who underwent anthropometric evaluation at age 4 years and in at least

104 evaluation, including collection of history, anthropometric examination, and biochemical tests.

105 ls explained minimal additional variation in anthropometric factors (null coefficients; adjusted R(2)

106 tively examined whether maternal and newborn anthropometric factors as reported by the mother are rel

107 family relatedness and offspring lifestyle, anthropometric factors, and inherited genetic variants (

108 rements were associated with demographic and anthropometric factors, and with the severity of RVGE sy

109 le nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive fa

110 xed models adjusted for sociodemographic and anthropometric factors.

111 1.89), independent of potential demographic, anthropometric, family history, reproductive, and lifest

112 The geographical, anthropometrics, FEV1, dyspnea, comorbidities, and healt

113 indings suggest that differences in maternal anthropometrics, gestational weight gain, and preterm bi

114 ral palsy, hearing or visual impairment, and anthropometric growth parameters.

115 uals, we identify novel loci associated with anthropometric, hematological, lipid, and glycemic trait

116 Anthropometrics identified a significant difference betw

117 e hand phenotype, and underscores a role for anthropometrics in characterizing sub-clinical brachydac

118 e widespread use of weight- and length-based anthropometric indexes as proxies for adiposity, little

119 However, it is unknown for other anthropometric indexes in young adults.

120 Anthropometric indexes included BMI, waist circumference

121 .The aim was to determine the association of anthropometric indexes with risks of inpatient and postd

122 Anthropometric indexes, energy intake, energy expenditur

123 not reduce the risk of death associated with anthropometric indexes.U6M infants at the highest risk o

124 re similar using body mass index (BMI) as an anthropometric indicator of nutritional status.

125 Although findings demonstrate improvement in anthropometric indicators during a period in which nutri

126 ception, and relevant nutritional and health anthropometric indicators in three Latin-American popula

127 The adjusted HRs using all anthropometric indicators were 1.43 (95% CI 0.53-3.87, p

128 Anthropometric indices (weight, height, mid-upper arm ci

129 Anthropometric indices currently used in practice explai

130 Nutritional status was assessed by anthropometric indices expressed in standard deviation s

131 Anthropometric indices including body mass index (BMI) a

132 Caucasians whose 35 clinical blood test and anthropometric indices matched the medical norm, we prov

133 whether novel shape measures can complement anthropometric indices when estimating waist skinfold th

134 us of children with CLD and to correlate the anthropometric indices with the severity of liver diseas

135 is study has limitations because some of the anthropometric information was obtained from retrospecti

136 ed for known risk factors (sociodemographic, anthropometric, lifestyle, medical history, and nutritio

137 avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demograph

138 Demographics, anthropometrics, liking ratings, and neural responses to

139 articipating in the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study.

140 ics and lifestyle) of parental education and anthropometric markers of childhood nutrition [leg lengt

141 including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference

142 urine metabolome, measures of physiology and anthropometrics measured prior to any dietary interventi

143 ewborns in the 10th percentile of each birth anthropometric measurement to that of those in the 90th

144 for antibiotic use, infection diagnosis, and anthropometric measurements (and thus BMI and obesity st

145 ple on the Bayley and Wolke scales and their anthropometric measurements (weight, length or height, a

146 Anthropometric measurements and %L through urine were co

147 at 1 year of age, determined from objective anthropometric measurements and defined according to Wor

148 al (DM)] or SGA at birth were evaluated with anthropometric measurements and health outcomes.

149 al [DM]) or SGA at birth were evaluated with anthropometric measurements and health outcomes.

150 tegrating comprehensive clinical assessment, anthropometric measurements and objective biochemical an

151 Both groups showed a significant decrease in anthropometric measurements and significant improvements

152 A significant reduction in anthropometric measurements and significant improvements

153 A statistically significant reduction in anthropometric measurements and significant improvements

154 We observe no differences between anthropometric measurements at birth.

155 etermined from regression equations based on anthropometric measurements derived from relatively smal

156 017 with information on milk consumption and anthropometric measurements from all low- and middle-inc

157 etween exposure to PBDEs via breast milk and anthropometric measurements in early childhood.

158 The shift to electronic recording of anthropometric measurements in electronic healthcare rec

159 arately and jointly in a "taste score"), and anthropometric measurements in older subjects with metab

160 The patients were evaluated for anthropometric measurements of body mass index, waist ci

161 There were no differences in anthropometric measurements or energy intakes between gr

162 Anthropometric measurements were converted into age- and

163 Lifestyle, diet, and anthropometric measurements were obtained at baseline an

164 can in the first trimester, and infant birth anthropometric measurements were obtained from hospital

165 o significant differences in weight loss and anthropometric measurements were seen between groups aft

166 Anthropometric measurements were taken at all study visi

167 Anthropometric measurements were taken at baseline and 5

168 Infant anthropometric measurements were taken every month for 6

169 Anthropometric measurements were taken weekly for 4 wk t

170 cy and general health status questionnaires, anthropometric measurements were taken, and a fasting bl

171 study visits (n = 246 children with > 1,400 anthropometric measurements).

172 spective contribution of insulin resistance, anthropometric measurements, and behavioral factors to b

173 collected clinical and epidemiological data, anthropometric measurements, and faecal samples to ident

174 me, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically

175 sperm membrane fatty acids, dietary intakes, anthropometric measurements, and physical activity were

176 Socio-demographic and anthropometric data [anthropometric measurements, blood pressure and total bo

177 f GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables,

178 Anthropometric measurements, demographic characteristics

179 body mass index was replaced with radiologic anthropometric measurements, greater skeletal muscle, an

180 nce of tools limits cleaning of longitudinal anthropometric measurements.

181 sampling methodology, survey questions, and anthropometric measurements.

182 sessments, microbiome assessments (16S), and anthropometric measurements.

183 significant, positive associations for all 9 anthropometric measures (HRs ranging from 1.08 [95% conf

184 ortcomings were observed in items related to anthropometric measures (the main variable of interest),

185 dinal association between NIS inhibitors and anthropometric measures [height, waist circumference, an

186 s observed for either of the obesity-related anthropometric measures after adjustment for lean body m

187 ps was compared between tools and related to anthropometric measures and clinical variables [e.g., le

188 11) for a follow-up assessment that included anthropometric measures and DNA collection.

189 al questionnaires; a research nurse recorded anthropometric measures and insurance status.

190 Anthropometric measures and self-reported life-style inf

191 Rs for postdischarge mortality for different anthropometric measures and thresholds.

192 tudies with long-term follow-up and repeated anthropometric measures are typically subject to missing

193 To explore the latter anthropometric measures as causal mediators, we also use

194 c risks were determined by serum markers and anthropometric measures at pre- and post-intervention.

195 as the obvious correlations between various anthropometric measures hamper identification of the cha

196 aPBDEs during pregnancy were associated with anthropometric measures in children aged 1-8 years.

197 ome (OSAS) risk with periodontal disease and anthropometric measures in Class III obese patients.

198 ast milk were not associated with early-life anthropometric measures in the PIN Babies cohort.

199 Primary outcomes were anthropometric measures of offspring undernutrition.

200 We investigated whether adding anthropometric measures to HbA1c would have stronger dis

201 lation-based surveys collect crucial data on anthropometric measures to track trends in stunting [hei

202 Iron, vitamin A, anemia, malaria, and anthropometric measures were assessed at baseline and at

203 siderable portion of children with subnormal anthropometric measures were not identified with all of

204 wners completed a questionnaire whilst their anthropometric measures were taken.

205 The combination of anthropometric measures with metabolic parameters, such

206 Socioeconomic data, anthropometric measures, and blood samples were collecte

207 on included socioeconomic and clinical data, anthropometric measures, blood pressure, human immunodef

208 Anthropometric measures, blood pressure, serum 25-hydrox

209 RONGKIDS)] compared with and were related to anthropometric measures, body composition, and clinical

210 renia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk fac

211 Various anthropometric measures, including height, have been ass

212 to be influenced by genetic association with anthropometric measures, inflating their overall measure

213 ntration <70 g/L) and individual-level (age, anthropometric measures, micronutrient deficiencies, mal

214 [n = 3,939]) with fetal ultrasound and birth anthropometric measures, parental smoking during pregnan

215 based on a regression that includes earlier anthropometric measures.

216 comes comprised recovery rate and additional anthropometric measures.

217 ssociations between maternal PBDEs and child anthropometric measures.

218 BMD), BMC, and bone area at the 4% tibia and anthropometric measures.No significant differences in th

219 conditions; schooling; child care behavior; anthropometric measures; and cognitive function were col

220 ithout IBS (control individuals), along with anthropometric, medical, and dietary information.

221 Anthropometric, metabolic, and periodontal parameters we

222 3DO model accuracy was compared with simple anthropometric models and precision was compared with DX

223 Anthropometric, nutrient, and activity assessments were

224 (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).

225 In addition, AMY1 CN was not associated with anthropometric or glycemic outcomes following either LCD

226 ct in men was not influenced by demographic, anthropometric, or metabolic factors; by the development

227 oncentrations were not associated with child anthropometric outcomes (all p-values > 0.05).

228 s related maternal component scores to child anthropometric outcomes at ages 5 (n = 326) and 7 (n = 3

229 mark and aged 0-5 years, we aimed to compare anthropometric outcomes in HIV-exposed but uninfected (H

230 Anthropometric outcomes, physical activity and sedentary

231 borns with probiotics is unlikely to improve anthropometric outcomes.

232 acronutrient/energy intakes) with changes in anthropometrics over hospitalization (days 1-8, 9-29, 30

233 Anthropometric parameters and frequently sampled oral gl

234 Agreement between BC/anthropometric parameters and MSTs was poor.

235 s (SNPs) were associated with functional and anthropometric parameters in a cohort of old community-d

236 Offspring anthropometric parameters included fetal, neonatal, and

237 we investigated how chemical properties and anthropometric parameters interact to influence the bioa

238 e in terms of walking speed, demographic and anthropometric parameters may lead to misinterpretation

239 nical scans is a valuable source to identify anthropometric parameters that influence BAT mass and ac

240 Central obesity-related anthropometric parameters were measured by analysing com

241 The mean values of z scores for all anthropometric parameters were significantly correlated

242 omes, postprandial symptoms, nutritional and anthropometric parameters, and overall quality of life a

243 ement traditional measures currently used in anthropometric practice to estimate central adiposity.

244 locate the HJC were developed using various anthropometrics predictors.

245 diovascular health, diabetes, kidney, liver, anthropometric profiles, blood, etc.

246 (best) to the lowest (worst) index values in anthropometric quality across countries and over time.

247 PDFF (PDFF(0)), initial anthropometrics, and anthropometric rates of change were evaluated as predict

248 uster analysis was performed on quantitative anthropometric, reproductive, and metabolic traits in a

249 5% confidence intervals (CIs), adjusting for anthropometric, reproductive, lifestyle, and socioeconom

250 Lean body mass was the predominant anthropometric risk factor for AF, whereas no associatio

251 er risk prediction tools including height or anthropometric risk factors can be used to improve scree

252 and AGP in WRA.Recent morbidity and abnormal anthropometric status are consistently associated with i

253 ween maternal malaria infection and maternal anthropometric status on the risk of LBW using pooled da

254 such as demographics, reported illness, and anthropometric status, in preschool children (PSC) (age

255 ow shape measurement can complement existing anthropometric techniques in the assessment of human for

256 atic review was to assess the performance of anthropometric tools to determine obesity in the general

257 previously reported signals for a different anthropometric trait.

258 p11.2 and 22q11.21, implicating at least one anthropometric trait.

259 981) study, and the Genetic Investigation of ANthropometric Traits (body mass index in 152,893 men an

260 Cs) representing body shape derived from six anthropometric traits (body mass index, height, weight,

261 5), smoking (rG=0.40, s.e.=0.06) and various anthropometric traits (for example, overweight, rG=-0.19

262 statistics from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and the Interna

263 es conducted by the Genetic Investigation of ANthropometric Traits (GIANT) Consortium and the UK Biob

264 rticipants from the Genetic Investigation of ANthropometric Traits (GIANT) Consortium genotyped on an

265 GG) Consortium, the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, the Tobacco an

266 Consistent with the Genetic Investigation of ANthropometric Traits (GIANT) dataset results, we didn't

267 Consortium (GLGC), Genetic Investigation of Anthropometric Traits (GIANT), and Meta-Analysis of Gluc

268 k (N = 336,473) and Genetic Investigation of ANthropometric Traits (GIANT, N = 339,224) to investigat

269 verlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis

270 using data from the Genetic Investigation of Anthropometric Traits and Psychiatric Genomics consortia

271 amine the broader allelic architecture of 12 anthropometric traits associated with height, body mass,

272 WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium.

273 ic simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well

274 large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples fro

275 notypes, the relationship with metabolic and anthropometric traits is markedly stronger for AN than f

276 d rare variants affecting body size and that anthropometric traits share genetic loci with developmen

277 fractive error reported shared genetics with anthropometric traits such as height, BMI and obesity.

278 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmenta

279 their previously reported associations with anthropometric traits were found to be confounded.

280 There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular a

281 nd metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of com

282 led hundreds of genetic loci associated with anthropometric traits, one trait at a time.

283 orders.Individual SNPs have small effects on anthropometric traits, yet the impact of CNVs has remain

284 tion summary statistics for 17 metabolic and anthropometric traits.

285 nalyses indicated positive correlations with anthropometric traits.

286 s between specific gene KOs and quantitative anthropometric traits.

287 type that is represented by a combination of anthropometric traits.

288 ehavioural, cardiovascular, demographic, and anthropometric traits.

289 sted associations between gene KOs and three anthropometric traits: body mass index (BMI), height and

290 Neonatal anthropometric values were measured at birth, and abdomi

291 d, and their association among them and with anthropometric variables and diet quality was studied.

292 ociated with MetS even after controlling for anthropometric variables and lipid profiles.

293 Anthropometric variables and PLIN1 genotypes were analyz

294 between both measures of SES and illnesses, anthropometric variables, psychiatric disorders, and cog

295 In young adults, obesity defined by various anthropometrics was consistently associated with localiz

296 ortion of children younger than 5 years with anthropometric wasting.

297 curves were used to analyze associations of anthropometric (weight, height, waist/hip circumferences

298 Maternal anthropometrics were assessed <=14 weeks of gestation.

299 tricted cubic splines for the mean change in anthropometric z scores were fit for each group.

300 We found no correlation between anthropometric z-scores and the mean IGF-1 and (25- OH D