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Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study.

ABSTRACT: BACKGROUND:Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese. METHODS:Nine thousand nine hundred sixty-two elderly individuals (age???60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices (body mass index [BMI], waist circumference [WC], waist-to-height ratio [WHtR]), novel anthropometric indices (visceral adiposity index [VAI], a body shape index [ABSI], body roundness index [BRI]) and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction. RESULTS:During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC?=?0.655) comparable with the other anthropometric indices (P?

SUBMITTER: Yang J

PROVIDER: S-EPMC6142386 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study.

Yang Jing J Wang Fei F Wang Jing J Han Xu X Hu Hua H Yu Caizheng C Yuan Jing J Yao Ping P Miao Xiaoping X Wei Sheng S Wang Youjie Y Chen Weihong W Liang Yuan Y Guo Huan H Zhang Xiaomin X Zheng Dan D Tang Yuhan Y Yang Handong H He Meian M

BMC geriatrics 20180917 1

<h4>Background</h4>Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese.<h4>Methods</h4>Nine thousand nine hundred sixty-two elderly individuals (age ≥ 60 years old) deri ...[more]

PMID: 30223783

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Project description:Aims/hypothesisThe hyperglycaemic clamp technique and the frequently sampled IVGTT are unsuitable techniques to assess beta cell function (BCF) in large cohorts. Therefore, the aim of this study was to evaluate the discriminatory ability of simple OGTT-based BCF indices for prediction of prediabetes (meaning impaired fasting glucose and/or impaired glucose tolerance) and type 2 diabetes.MethodsGlucose metabolism status was assessed by 2 h 75 g OGTT at baseline (n = 476, mean age 59.2 years, 38.7% women) and after 7 years of follow-up (n = 416) in the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) study (1999-2009). Baseline plasma glucose, insulin and C-peptide values during OGTTs were used to calculate 21 simple indices of BCF. Disposition indices (BCF index × Matsuda index), to compensate for the prevailing level of insulin resistance, were calculated for the BCF indices with the best discriminatory abilities. The discriminatory ability of the BCF indices was estimated by the area under the receiver operating characteristics curve (ROC AUC) with an outcome of incident prediabetes (n = 73) or type 2 diabetes (n = 60 and n = 18 cases, respectively, in individuals who were non-diabetic or had normal glucose metabolism at baseline).ResultsFor incident prediabetes (n = 73), all ROC AUCs were less than 70%, whereas for incident type 2 diabetes, I30/I0, CP30/CP0, ΔI30/ΔG30, ΔCP30/ΔG30 (where I, CP and G are the plasma concentrations of insulin, C-peptide and glucose, respectively, at the times indicated), and corrected insulin response at 30 min had ROC AUCs over 70%. In at-baseline non-diabetic individuals, disposition indices ΔI30/ΔG30, ΔCP30/ΔG30 and corrected insulin response at 30 min had ROC AUCs of over 80% for incident type 2 diabetes. Moreover, these BCF disposition indices had significantly better discriminatory abilities for incident type 2 diabetes than the Matsuda index alone.Conclusions/interpretationBCF indices reflecting early-phase insulin secretion have the best ability to discriminate individuals who will develop prediabetes and type 2 diabetes. Of these, ΔCP30/ΔG30, often referred to as the C-peptidogenic index, performed consistently well.

Project description:BackgroundObesity is protective of bone health; however, abdominal obesity is associated with a higher fracture risk. Little is known about whether body composition protects or adversely affects osteoporotic fractures because of practical issues regarding assessment tools. This study aimed to evaluate the association of predicted body composition with fracture risk to determine the distinctive and differing effects of muscle or fat mass on bone health outcomes in the general population.MethodsThis population-based, longitudinal cohort study used 2009-2010 Korean National Health Insurance Service data and follow-up data from 1 January 2011 to 31 December 2013, to determine the incidence of osteoporotic fracture (total, spine, and non-spine) defined using the International Classification of Diseases, Tenth Revision codes. The study participants were aged ≥50 years (men, 158 426; women, 131 587). The predicted lean body mass index (pLBMI), appendicular skeletal muscle index (pASMI), and body fat mass index (pBFMI) were used to assess body composition, using anthropometric prediction equations.ResultsOver a 3 year follow-up, we identified 2350 and 6175 fractures in men and women, respectively. The mean age of the participants was 60.2 ± 8.3 and 60.7 ± 8.4 years in men and women, respectively. In a multivariable-adjusted Cox proportional hazards regression model, increasing pLBMI or pASMI was significantly associated with a decreased risk of total fractures in men and women. When comparing individuals in the lowest pLBMI and pASMI (reference groups), men with the highest pLBMI and pASMI had adjusted hazard ratios of 0.63 [95% confidence interval (CI) 0.47-0.83] and 0.62 (95% CI 0.47-0.82), and women with the highest pLBMI and pASMI had adjusted hazard ratios of 0.72 (95% CI 0.60-0.85) and 0.71 (95% CI 0.60-0.85), respectively, for total fractures. The pBFMI had no significant association with total fractures in men or women. Regarding sex-specific or site-specific differences, the protective effects of the pLBMI and pASMI on fractures were greater in men and reduced the risk of spinal fractures. An increased pBFMI was associated with an increased risk of spinal fractures in women.ConclusionsAn increased pLBMI or pASMI was significantly associated with decreased total osteoporotic fracture risk; however, the pBFMI showed no statistically significant association. Muscle mass was more important than fat mass in preventing future osteoporotic fractures based on anthropometric prediction equations.

Project description:The objectives of this study were to analyse the capacity of different anthropometric indices to predict vascular ageing and this association in Spanish adult population without cardiovascular disease. A total of 501 individuals without cardiovascular disease residing in the capital of Salamanca (Spain) were selected (mean age: 55.9 years, 50.3% women), through stratified random sampling by age and sex. Starting from anthropometric measurements such as weight, height, and waist circumference, hip circumference, or biochemical parameters, we could estimate different indices that reflected general obesity, abdominal obesity, and body fat distribution. Arterial stiffness was evaluated by measuring carotid-femoral pulse wave velocity (cf-PWV) using a SphygmoCor® device. Vascular ageing was defined in three steps: Step 1: the participants with vascular injury were classified as early vascular ageing (EVA); Step 2: classification of the participants using the 10 and 90 percentiles of cf-PWV in the study population by age and sex in EVA, healthy vascular ageing (HVA) and normal vascular ageing (NVA); Step 3: re-classification of participants with arterial hypertension or type 2 diabetes mellitus included in HVA as NVA. The total prevalence of HVA and EVA was 8.4% and 21.4%, respectively. All the analysed anthropometric indices, except waist/hip ratio (WHpR), were associated with vascular ageing. Thus, as the values of the different anthropometric indices increase, the probability of being classified with NVA and as EVA increases. The capacity of the anthropometric indices to identify people with HVA showed values of area under the curve (AUC) ≥ 0.60. The capacity to identify people with EVA, in total, showed values of AUC between 0.55 and 0.60. In conclusion, as the values of the anthropometric indices increased, the probability that the subjects presented EVA increased. However, the relationship of the new anthropometric indices with vascular ageing was not stronger than that of traditional parameters. Therefore, BMI and WC can be considered to be the most useful indices in clinical practice to identify people with vascular ageing in the general population.

Project description:Objectives:Dietary phenylalanine restriction is the main treatment of phenylketonuria (PKU, OMIM 261600). There are a number of studies which have demonstrated growth retardation in these patients, and some are in contrast. This study was performed to assess the growth parameters of treated PKU patients. Materials & Methods:This cross-sectional study was performed between 2015 and 2017 to compare growth indices in PKU patients in our clinics with normal age and sex matched controls. Weight, height, head circumference (HC), weight for height and BMI (weight/height2) were measured and converted into Z-scores. We assessed differences between patients and controls' anthropometric indexes in all patients and separately in patients who were diagnosed by newborn screening program and patients who were diagnosed after presentation of clinical manifestations in comparison with age and sex-matched controls. Also, this difference was assessed separately in patients aged two years and less. Correlations between pretreatment plasma phenylalanine concentrations mean plasma phenylalanine concentrations and anthropometric parameters were analyzed in the patients. Results:Overall, 209 under-treatment PKU patients (103 males, 106 females; mean age 9.29 ± 8.7 years) and 216 controls (109 males and 107 females; mean age 8.98 ± 8.62 years) matched in terms of age, sex and birth weight were enrolled in this study. In general, 130 patients were diagnosed by newborn screening and 79 were diagnosed when they became symptomatic before the screening program. A significant difference (p=0.000) was found only in HC z-score and weight for height z-score in comparison with the control group, when we assessed all patients. We did not find any significant differences in any of the anthropometric indexes between cases and controls who were aged 2 years old and less. Head circumference SDS and weight for height SDS were significantly different when patients and controls who were more than 2 years old were compared. Mean HC was significantly lower in patients, while BMI SDS, weight SDS, and weight for height SDS were significantly higher in PKU patients in comparison with the control group when patients who were diagnosed in newborn screening were assessed. Head circumference SDS, BMI, height SDS and difference between patients' height SDS and mid parental height SDS had significantly lower mean scores in comparison with those of the control group, while mean weight SDS was significantly higher compared to controls when patients who were diagnosed after clinical presentation were assessed. Mean phenylalanine was not correlated with anthropometric indices, while there was a correlation between pretreatment phenylalanine and HC. Conclusion:Disparities in anthropometric indexes changes observed in different studies may be due to diverse diet protocols, availability of various specific products and micronutrient substitutes.

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Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study.

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Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study.

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