Association between predicted fat mass, predicted lean mass, predicted percent fat and type 2 diabetes mellitus in Japanese adults: a retrospective study
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SUBMITTER: Tang J
PROVIDER: S-EPMC11022471 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:ObjectivesWith body mass index (BMI) failing to distinguish the mass of fat from lean, several novel predicted equations for predicted fat mass (FM), predicted lean mass (LM) and predicted per cent fat (PF) were recently developed and validated. Our aim was to explore whether the three novel parameters could better predict diabetes mellitus (DM) than the commonly used obesity indicators, including BMI, waist circumference, hip circumference and waist-hip ratio.DesignA 15-year prospective cohort was used.SettingIt was a prospective cohort, consisting of a general Chinese population from 1992 to 2007.ParticipantsThis cohort enrolled 711 people. People suffering from DM at baseline (n=24) were excluded, and 687 non-diabetics with complete data were included to the analysis.Primary outcomeNew-onset DM.ResultsAfter the follow-up, 74 (48 men and 26 women) incidences of DM were documented. For men, the adjusted HRs were 1, 5.19 (p=0.003) and 7.67 (p<0.001) across predicted PF tertiles; 1, 2.86 (p=0.029) and 5.60 (p<0.001) across predicted FM tertiles; 1, 1.21 (p=0.646) and 2.27 (p=0.025) across predicted LM tertiles. Predicted FM performed better than other commonly used obesity indicators in discrimination with the highest Harrell's C-statistic among all the body composition parameters. Whereas, for women, none of the three novel parameters was the independent predictor.ConclusionPredicted PF, predicted LM and predicted FM could independently predict the risk of DM for men, with predicted FM performing better in discrimination than other commonly used obesity indicators. For women, larger samples were further needed.
| S-EPMC9174812 | biostudies-literature
Project description:BACKGROUND:Previous studies have found that predicted fat mass and lean body mass may act differently on adverse events. However, the cardiovascular prognostic value of lean body mass and fat mass in patients with type 2 diabetes mellitus (T2DM) has not yet been investigated. We sought to investigate the relation between predicted lean body mass or fat mass and the risk of cardiovascular disease in patients with T2DM. METHODS:We conducted a post hoc analysis of data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study to investigate the relation between the predicted lean body mass or fat mass and major adverse cardiovascular events in patients with T2DM. We used sex-specific quartiles of predicted lean body mass index (BMI; kg/m2) and fat mass index (kg/m2). We defined a major adverse cardiovascular event as a composite of nonfatal myocardial infarction, nonfatal stroke or death from cardiovascular causes. RESULTS:After a mean follow-up period of 8.8 years, we found that a major cardiovascular event occurred in 1801 of 10 251 patients (17.8%). Predicted lean BMI was not associated with major cardiovascular events (p = 0.34). Compared with patients in the first quartile (incidence rate 16.4%; 17.2%, 17.5% and 19.8% for the second, third and four quartiles, respectively) of predicted fat mass index, those in the fourth quartile had a hazard ratio of 1.53 (95% confidence interval 1.23-1.91). INTERPRETATION:In patients with T2DM, we found that predicted fat mass had a strong positive association with a higher risk of a major adverse cardiovascular event. Increasing lean body mass did not have a protective role. Trial registration: ClinicalTrials.gov., no. NCT00000620.
| S-EPMC6763326 | biostudies-literature
Project description:BackgroundLean body mass (LBM) and fat mass (FM) have been shown to have different associations with several chronic diseases but little is known about the sex-specific association of LBM and FM with diabetic nephropathy (DN) risk among participants with diabetes.MethodsParticipants from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study was used in a post hoc analysis to examine the association of predicted LBM index (LBMI) and FM index (FMI) with incident DN risk (defined as a composite outcome of three types of predefined DN). Because of sex differences in body composition, analyses were conducted separately using sex-specific quartiles of predicted LBMI and FMI.ResultsOf the 9,022 participants with type 2 diabetes (5,575 men and 3,447 women) included in this study, 5,374 individuals developed DN (3,396 in men and 1,978 in women). Higher quartiles of LBMI were associated with a reduced risk of DN while higher quartiles of FMI were associated with an increased higher risk of DN among men but not women. Compared with the lowest quartile, the fully adjusted hazard ratios (HRs) and 95% confidence intervals (CIs)for the highest quartile of predicted LBMI and FMI were respectively 0.83 (95% CI 1.71 - 0.96) and 1.23 (95% CI 1.06-1.43) among men; and 0.92 (95% CI 0.63 - 1.33) and 1.14 (95% CI 0.79 - 1.63) among women.ConclusionsAmong participants with diabetes, predicted LBMI was inversely associated with risk of DN while predicted FMI was positively associated with an increased risk of incident DN among men but not women. Trial registration: ClinicalTrials.gov., no. NCT00000620.
| S-EPMC8578879 | biostudies-literature
Project description:ObjectiveThe relationship between body composition fat mass (FM) and lean body mass (LBM) and diabetes risk is currently debated, and the purpose of this study was to examine the association of predicted FM and LBM with diabetes in both sexes.MethodsThe current study was a secondary analysis of data from the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) cohort study of 15,463 baseline normoglycemic participants. Predicted LBM and FM were calculated for each participant using anthropometric prediction equations developed and validated for different sexes based on the National Health and Nutrition Examination Survey (NHANES) database, and the outcome of interest was diabetes (types not distinguished) onset. Multivariate Cox regression analyses were applied to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of predicted FM and LBM with diabetes risk and further visualized their associations using a restricted cubic spline function.ResultsThe incidence density of diabetes was 3.93/1000 person-years over a mean observation period of 6.13 years. In women, predicted LBM and FM were linearly associated with diabetes risk, with each kilogram increase in predicted LBM reducing the diabetes risk by 65% (HR 0.35, 95%CI 0.17, 0.71; P < 0.05), whereas each kilogram increase in predicted FM increased the diabetes risk by 84% (HR 1.84, 95%CI 1.26, 2.69; P < 0.05). In contrast, predicted LBM and FM were non-linearly associated with diabetes risk in men (all P for non-linearity < 0.05), with an L-shaped association between predicted LBM and diabetes risk and a saturation point that minimized the risk of diabetes was 45.4 kg, while predicted FM was associated with diabetes risk in a U-shape pattern and a threshold point with the lowest predicted FM-related diabetes risk was 13.76 kg.ConclusionIn this Asian population cohort, we found that high LBM and low FM were associated with lower diabetes risk according to anthropometric equations. Based on the results of the non-linear analysis, we believed that it may be appropriate for Asian men to keep their LBM above 45.4 kg and their FM around 13.76 kg.
| S-EPMC10203423 | biostudies-literature
Project description:BackgroundRelative fat mass (RFM) represents a newly developed sex-specific anthropometric formula to estimate total body fat percentage. Nonetheless, research examining the correlation between RFM and the risk of diabetes remains scarce. This research assessed the link between RFM and DM risk within the Japanese demographic.MethodsFrom 2004 to 2015, 15,462 Japanese individuals without diabetes underwent physical evaluations at Murakami Memorial Hospital. The relationship between RFM and the onset of diabetes was analyzed separately using Cox proportional-hazards regression models. This study employed Cox proportional hazards regression incorporating cubic spline functions and smooth curve fitting to detect non-linear associations between RFM and new cases of diabetes, categorized by sex. Sensitivity analyses were performed to confirm the robustness of the link between RFM and incident diabetes.ResultsAfter controlling for confounding factors, a significant positive correlation between RFM and diabetes risk was found in women (HR: 1.13, 95%CI: 1.04-1.24, P = 0.0061), while the association in men was not statistically significant (HR: 1.05, 95%CI: 0.98-1.13, P = 0.1511). Additionally, a non-linear relationship between RFM and the incidence of diabetes was detected in both genders. The RFM threshold was identified at 39.23 for women and 23.08 for men. For women, HR was 1.11 (95%CI: 1.01-1.21) below the threshold and 1.39 (95%CI: 1.17-1.65) above it. In men, an RFM above 23.08 was positively related to diabetes risk (HR: 1.16, 95%CI: 1.06-1.28, P = 0.0012), whereas an RFM below this point did not show a significant association (HR: 0.98, 95%CI: 0.91-1.06, P = 0.5899).ConclusionOur findings suggest a non-linear relationship and threshold effect between RFM and the risk of diabetes. These findings imply that maintaining RFM at lower levels may be beneficial in mitigating the onset of DM.
| S-EPMC11461832 | biostudies-literature
Project description:BackgroundStudies on the prospective association of body composition with mortality in US general populations are limited. We aimed to examine this association by utilizing data from the National Health and Nutrition Examination Survey (NHANES), a representative sample of US adults, linked with data from the National Death Index.MethodsWe analysed data of NHANES 1988-1994 and 1999-2014, with 55 818 participants [50.6% female, baseline mean age: 45.0 years (SE, 0.2)]. Predicted fat mass and lean mass were calculated using the validated sex-specific anthropometric prediction equations developed by the NHANES based on individual age, race, height, weight, and waist circumference. Body composition and other covariates were measured at only one time point. Multivariable Cox regression was used to investigate the associations of predicted fat mass and lean mass with overall and cause-specific mortality, adjusting for potential confounders. Interactions between age and body composition on mortality were examined with likelihood ratio testing.ResultsMean predicted fat mass was 24.1 kg [95% confidence interval (CI): 23.9-24.3) for male participants and 29.9 kg (95% CI: 29.6-30.1) for female participants, while mean predicted lean mass was 59.3 kg (95% CI: 59.1-59.5) for male participants and 41.7 kg (95% CI: 41.5-41.8) for female participants. During a median period of 9.7 years from the survey, 10 408 deaths occurred. When predicted fat and lean mass were both included in the model, predicted fat mass showed a U-shaped association with all-cause mortality, with significantly higher risk at two ends: Quintile 1 (HR, 1.17; 95% CI: 1.05-1.31), Quintile 2 (HR, 1.14; 95% CI: 1.04-1.26) and Quintile 5 (HR, 1.37; 95% CI: 1.12-1.68) compared with Quintile 3. In contrast, predicted lean mass showed a L-shaped association with all-cause mortality, with higher mortality in those with lower lean mass: Quintile 1 (HR, 1.64; 95% CI: 1.46-1.83) and Quintile 2 (HR, 1.29; 95% CI: 1.18-1.42) compared with Quintile 3. Similar results were found for cardiovascular, cancer, and respiratory cause-specific mortality. Age was a significant modifier: There was a monotonic positive association of predicted fat mass with mortality in younger participants (<60 years), but an approximate J-shaped association in older participants (≥60 years) (P interaction <0.001); there was a stronger inverse association between predicted lean mass and mortality in older participants (≥60) compared with those <60 years (P interaction <0.001).ConclusionsIn this US general population, predicted fat mass and lean mass were independent predictors for overall and cause-specific mortality. Age was a significant modifier on the associations.
| S-EPMC8978015 | biostudies-literature
Project description:An inverse association between body mass index (BMI) and risk of lung cancer has been reported. However, the association of body composition such as fat mass (FM) and lean body mass (LBM) with risk of lung cancer has not been fully investigated. Using two large prospective cohort studies (Nurses' Health Study, 1986-2014; Health Professionals Follow-up Study, 1987-2012) in the United States, we included 100,985 participants who were followed for occurrence of lung cancer. Predicted FM and LBM derived from validated anthropometric prediction equations were categorized by sex-specific deciles. During an average 22.3-year follow-up, 2615 incident lung cancer cases were identified. BMI showed an inverse association with lung cancer risk. Participants in the 10th decile of predicted FM and LBM had a lower risk of lung cancer compared with those in the 1st decile, but when mutually adjusted for each other, predicted FM was not associated with lung cancer risk (adjusted hazard ratio [aHR] = 0.98, 95% confidence interval [CI] 0.72-1.35; P(trend) = 0.97) whereas predicted LBM had an inverse association (aHR = 0.73, 95% CI 0.53-1.00; P(trend) = 0.03), especially among participants who were current smokers or had smoked in the previous 10 years (aHR = 0.55, 95% CI 0.36-0.84; P(trend) = 0.008). In conclusion, BMI was inversely associated with lung cancer risk. Based on anthropometric prediction equations, low LBM rather than low FM accounted for the inverse association between BMI and lung cancer risk.
| S-EPMC7504685 | biostudies-literature
Project description:BackgroundLittle is known about the association of changes in two body components, muscle and fat mass, with the risk of cardiovascular disease (CVD) among young adults. We investigated the association of changes in predicted lean body mass index (LBMI), appendicular skeletal muscle mass index (ASMI), and body fat mass index (BFMI) with the development of CVD among young adults.MethodsThis nationwide, population-based cohort study included 3 727 738 young adults [2 406 046 (64.5%) men and 1 321 692 (35.5%) women] aged 20-39 years without a previous history of CVD who underwent two health screening examinations during 2009-2010 and 2011-2012. Using validated and robust prediction equations, we calculated the changes in predicted LBMI, ASMI, and BFMI from the first to the second examinations.ResultsThe mean (SD) age was 32.2 (4.9) years, and 2 406 046 (64.5%) of the participants were men. A total of 23 344 CVD events were detected during 22 257 632 person-years of follow-up. Each 1 kg/m2 increase in predicted LBMI and ASMI change was associated with a reduced risk of CVD among men [adjusted hazard ratio (aHR): 0.86, 95% confidence interval (CI) 0.82-0.91; aHR: 0.76, 95% CI 0.69-0.82, respectively] and women (aHR: 0.77, 95% CI 0.63-0.95; aHR: 0.75, 95% CI 0.59-0.96). Each 1 kg/m2 increase in predicted BFMI change was associated with an increased risk of CVD among men (aHR: 1.16, 95% CI 1.10-1.22) and women (aHR: 1.32, 95% CI 1.06-1.65). In both sexes, decreases in predicted LBMI and ASMI were associated with greater CVD risk, and decreased predicted BFMI was associated with a reduced CVD risk. Those who maintained their BMI between -1 and +1 kg/m2 also had a decreased risk of CVD per 1 kg/m2 increase in predicted LBMI and ASMI change among men (aHR: 0.86, 95% CI 0.80-0.92; aHR: 0.85, 95% CI 0.76-0.95) and women (aHR: 0.62, 95% CI 0.47-0.83; aHR: 0.59, 95% CI 0.44-0.80) and had a greater risk of CVD per 1 kg/m2 increase in predicted BFMI change among men (aHR: 1.17, 95% CI 1.10-1.25) and women (aHR: 1.64, 95% CI 1.20-2.23). Regardless of changes in weight, such as from normal to obese or vice versa, these results were consistent.ConclusionsAmong young adults, increased predicted muscle mass or decreased predicted fat mass were associated with a reduced risk of development of CVD. Decreased predicted muscle mass or increased predicted fat mass were associated with an elevated risk of development of CVD.
| S-EPMC8978024 | biostudies-literature
Project description:IntroductionFew studies have deciphered whether lean body mass (LBM) or fat mass (FM) is predominantly responsible for the body mass index (BMI)-hypertension association. This study aimed to assess the associations of predicted LBM and FM with hypertension risk among Chinese male adults.MethodsA prospective cohort study was conducted among 2,812 male participants free of hypertension in the Fangchenggang Area Males Health and Examination Survey in 2009. We performed multivariable Cox models and restricted cubic spline to examine the associations of predicted LBM and FM and BMI with hypertension, and to further explore the mediating roles of lipid and glycemic traits in the relationship between predicted FM and blood pressure.ResultsOf 1,238 participants included in the cohort study, 306 (24.8%) hypertension cases were identified during a median follow-up of 3.8 years, with an incidence rate of 7.0 per 100 person-years. A positive linear-shaped association was consistently observed between BMI and hypertension (p for trend <0.001). Multivariable-adjusted Cox models including predicted LBM and FM observed a positive association between predicted FM and hypertension. Compared with those in the lowest quartile of predicted FM, men in the highest quartile had a hazard ratio (HR) of 1.83 (95% confidence interval (CI): 1.13-2.97) for hypertension. The HR per standard deviation increase of BMI and predicted FM was 1.11 (95% CI: 1.04-1.19) above 23.1 kg/m2 and 1.05 (95% CI: 1.02-1.15) above 14.6 kg, respectively. However, predicted LBM was not associated with hypertension. In addition, high-density lipoprotein cholesterol (HDL-c) and fasting blood glucose (FBG) mediated the relationship of predicted FM with systolic blood pressure, with a mediation ratio of 37.1% and 8.2%, respectively. Furthermore, total cholesterol (TC) and triglyceride (TG) positively mediated the association of predicted FM with diastolic blood pressure, with a mediation ratio of 9.5% and 9.9%, respectively.ConclusionHigher predicted FM might play a central role in the positive linear relationship of the BMI-hypertension association in Chinese male adults, and the link from predicted FM to blood pressure was partially mediated by TC, TG, HDL-c, and FBG.
| S-EPMC9669944 | biostudies-literature
Project description:BackgroundPrevious findings about lean body mass (LBM) and cognitive function remain unclear. We aimed to examine this association by using data from the National Health and Nutrition Examination Survey (NHANES).MethodsUsing data from the NHANES 2011-2014, we conducted logistic regression models to investigate the relation between the predicted LBM and domain-specific cognitive function assessed by Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimer's Disease Word Learning test (CERAD-WL) and Delayed Recall test (CERAD-DR), and Animal Fluency (AF) for information processing speed, memory, and executive function, respectively. Cognitive impairment was defined as the lowest quartile of each cognitive test in the total population. Sex-stratified analysis was further made.ResultsA total of 2955 participants aged 60 and above (mean [SD] age, 69.17[0.20] years; 1511 female [51.13%]) were included in the study. After being adjusted for social economic factors, anthropometric parameters, and diseases, we found a positive association between predicted LBM and information processing speed (Odds ratio of DSST impairment= 0.95, 95%CI= 0.91 to 0.99) regardless of body mass index and sex. Compared with patients in the first quartile of predicted LBM, those in the fourth quartile had an odds ratio of 0.355 (95% confidence interval 0.153-0.822) for DSST impairment. No significant relation in other cognitive tests and predicted LBM was found whether stratified by sex or not.ConclusionOur findings point to the association between predicted lean body mass and cognitive dysfunction in information processing speed, which could be used for early detection and prevention of deterioration of cognitive function among older adults.
| S-EPMC10358760 | biostudies-literature