Predicted lean body mass, fat mass and risk of lung cancer: prospective US cohort study

Association of Metabolic Syndrome With Risk of Lung Cancer: A Population-Based Prospective Cohort Study

BACKGROUND

Both the incidence of lung cancer and the prevalence of metabolic syndrome (MetS) have been increasing worldwide. The relationship between MetS and lung cancer remains controversial.

RESEARCH QUESTION

What is the risk of lung cancer associated with MetS and its components?

STUDY DESIGN AND METHODS

Multivariable Cox regression models were used to estimate the hazard ratio (HR) of MetS-related variables on lung cancer risk, both overall and by histologic subtype, in the UK Biobank. Stratified analyses were conducted by sex, tobacco use status, and use of medication. HR curves were used to test the nonlinear associations between the metabolic markers and the risk of lung cancer.

RESULTS

Of the 331,877 participants included in this study, a total of 77,173 participants had a diagnosis of MetS at enrollment. During a median follow-up of 10.9 years, lung cancer as the primary site developed in 2,425 participants. The HRs of MetS were 1.21 (95% CI, 1.09-1.33), 1.28 (95% CI, 1.10-1.50), and 1.16 (95% CI, 0.94-1.44) for the overall risk of lung cancer, adenocarcinoma, and squamous cell carcinoma, respectively. The HRs increased with the number of metabolic abnormalities from 1.11 to approximately 1.4 or 1.5 for those with one to five disorders. Positive association with lung cancer was observed for low high-density lipoprotein cholesterol (HDL-C), elevated waist circumference, and hyperglycemia. The relationship between MetS and lung cancer was modified by sex, with a stronger effect in female patients (P = .031). The risk of lung cancer resulting from MetS was elevated mainly among individuals who used tobacco, although the modification effect of tobacco use was not statistically significant. A nonlinear association was found between lung cancer and HDL-C, waist circumference, and glycated hemoglobin.

INTERPRETATION

The increased risk of lung cancer associated with MetS suggests the importance of taking metabolic status and markers into consideration for the primary prevention of lung cancer and the selection of high-risk populations for lung cancer screening.

Predicted lean body mass trajectories, and cancer risk and cancer-specific and all-cause mortality: A prospective cohort study

BACKGROUND

Although many studies have investigated the association between body composition, cancer risk and mortality, predicting these risks through a single body composition measurement undoubtedly increases the limitations of the study. Few studies have explored the association between the trajectory of changes in body composition and the risk of cancer and death. We aimed to explore the association of predicted lean mass trajectories with cancer risk, cancer-specific mortality and all-cause mortality.

METHODS

The participants in this study were all from the Kailuan cohort, a prospective, periodic, resurvey cohort study initiated in 2006. Latent mixture modelling was used to identify predicted lean mass trajectories for 2006-2010. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) of the Cox proportional hazard models were used to describe the association between predicted lean mass trajectories and cancer risk and cancer-specific and all-cause mortality during follow-up (2010-2021).

RESULTS

A total of 44 374 participants (average age, 53.01 ± 11.41 years, 78.99% men and 21.01% women) were enrolled in this study. Five distinct trajectories were identified: low-stable (n = 12 060), low-increasing (n = 8027), moderately stable-decreasing (n = 4725), moderately stable-increasing (n = 8053) and high-stable (n = 11 509). During the 11-year follow-up period, 2183 cancer events were recorded. After adjusting for age, predicted fat mass in 2010, sex, BMI, sedentary, physical activity, smoke, alcohol use, salt consumption, high-fat diet, high-sensitivity C-reactive protein, serum creatinine, family history of tumour, hypertension, diabetes mellitus, compared with the low-stable group, participants in the low-increasing group (HR = 0.851, 95% CI, 0.748-0.969), moderately stable-increasing group (HR = 0.803, 95% CI, 0.697-0.925) and high-stable group (HR = 0.770, 95% CI, 0.659-0.901) had a lower cancer risk, but not in the moderately stable-decreasing group (HR = 0.864, 95% CI, 0.735-1.015). Compared with the low-stable group, the risk of cancer-specific mortality was reduced by 25.4% (8.8-38.9%), 36.5% (20.3-49.4%) and 35.4% (17.9-49.2%), and the risk of all-cause mortality was reduced by 24.2% (16.9-30.8%), 37.0% (30.0-43.2%) and 47.4% (41.0-53.1%) in the low-increasing, moderately stable-increasing group and high-stable groups, respectively.

CONCLUSIONS

Predicted lean mass trajectories may be closely associated with cancer risk and cancer-specific and all-cause mortality. Regular monitoring of body composition is necessary.

Body composition and risk of liver cancer: a population-based prospective cohort study on gender difference

Background

Obesity is a common and highly convincing risk factor for many cancers, including liver cancer. Sex disparities in the body composition and regulatory mechanisms involved in energy homeostasis may contribute to the difference in the incidence of cancer. However, evidence on the gender-specific association between body composition and liver cancer incidence is limited. We performed this study to investigate the linear and non-linear associations of body composition with liver cancer risk by gender.

Materials and methods

This prospective analysis included 4,75,659 participants free of cancer, based on the UK Biobank. We used Cox proportional hazard models to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for potential confounders. Restricted cubic spline was performed to investigate the potential non-linear associations.

Results

During a median follow-up, 275 cases (174 male patients and 101 female patients) of liver cancer were identified. Male patients in the highest body fat percentage group are more likely to develop liver cancer (HR = 1.89, 95% CI: 1.17-3.03) compared with those in the lowest group. The one-unit increase of whole-body fat mass, arm fat mass, and trunk fat mass was associated with 1.03-, 1.14-, and 1.05-fold increased risk of liver cancer in male subjects, respectively. U-shaped associations of body composition with liver cancer risk were observed in the female subjects. Both high and low levels of whole-body fat-free mass, particularly in the arm and trunk, were associated with an increased risk of liver cancer.

Conclusion

This study found a gender-specific association between body composition and liver cancer risk and provided evidence for individualized weight management for the prevention of liver cancer.

Association of predicted fat mass and lean body mass with diabetes: a longitudinal cohort study in an Asian population

Objective

The 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.

Methods

The 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.

Results

The 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.

Conclusion

In 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.

Artificial Neural Network Models for Accurate Predictions of Fat-Free and Fat Masses, Using Easy-to-Measure Anthropometric Parameters

Abdominal fat and fat-free masses report a close association with cardiometabolic risks, therefore this specific body compartment presents more interest than whole-body masses. This research aimed to develop accurate algorithms that predict body masses and specifically trunk fat and fat-free masses from easy to measure parameters in any setting. The study included 104 apparently healthy subjects, but with a higher-than-normal percent of adiposity or waist circumference. Multiple linear regression (MLR) and artificial neural network (ANN) models were built for predicting abdominal fat and fat-free masses in patients with relatively low cardiometabolic risks. The data were divided into training, validation and test sets, and this process was repeated 20 times per each model to reduce the bias of data division on model accuracy. The best performance models used a maximum number of five anthropometric inputs, with higher R² values for ANN models than for MLR models (R² = 0.96-0.98 vs. R² = 0.80-0.94, p = 0.006). The root mean square error (RMSE) for all predicted parameters was significantly lower for ANN models than for MLR models, suggesting a higher accuracy for ANN models. From all body masses predicted, trunk fat mass and fat-free mass registered the best performance with ANN, allowing a possible error of 1.84 kg for predicting the correct trunk fat mass and 1.48 kg for predicting the correct trunk fat-free mass. The developed algorithms represent cost-effective prediction tools for the most relevant adipose and lean tissues involved in the physiopathology of cardiometabolic risks.

Body mass index and additional risk factors for cancer in adults with cystic fibrosis

BACKGROUND

Adults with cystic fibrosis (CF) have an increased risk of a variety of cancers, notably gastrointestinal cancers. In CF higher body mass index (BMI) is associated with improved long-term outcomes, yet in the general population high BMI is associated with increased cancer risk. We aimed to delineate associations between BMI and other factors with cancer risk in adults with CF.

METHODS

This was a retrospective cohort study using CF Foundation Patient Registry data from 1992 to 2015. Data were collected on age, sex, CFTR mutation class, pancreatic insufficiency, and annualized data on BMI and FEV1. The primary analysis was the association between BMI and cancer, with secondary analyses focused on BMI trajectory. Multivariable logistic regression was performed, with analyses stratified by history of transplant.

RESULTS

Of 26,199 adults with CF, 446 (1.7%) had cancer diagnosed by histology at a mean age of 40.0 years (SD 12.2), with a higher proportion of transplanted patients developing cancer (137 (3.8%) v 309(1.4%), p < 0.001). Among non-transplanted patients, there was no association between BMI and cancer (p for trend = 0.43). Pancreatic insufficiency (p < 0.01) and higher FEV1 (p < 0.01) were associated with increased cancer risk. In transplanted patients, higher BMI was associated with reduced risk of cancer (p for trend = 0.04). Older age was associated with increased risk in both groups (p < 0.001). BMI trajectories were not associated with cancer risk in either group.

CONCLUSION

Higher BMI is associated with a reduced risk of cancer in transplanted adults with CF. Pancreatic insufficiency is a risk factor for cancer in non-transplanted CF patients.

Metabolic Obesity Phenotypes and Risk of Lung Cancer: A Prospective Cohort Study of 450,482 UK Biobank Participants

(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33–7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61–0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70–0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Changes in fat mass and lean body mass and outcomes in type 2 diabetes mellitus

Previous studies have found that fat mass and lean body mass may act differently on the prognosis in patients type 2 diabetes mellitus (T2DM). However, the change of fat mass and lean body mass on prognosis in T2DM patients has not yet been investigated. We performed a Post hoc analysis of data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Cox proportional hazards models were used to study the relationship between tertiles of the change trend in lean body mass index (LBMI, kg/m²/year) or fat mass index (FMI, kg/m²/year) and major cardiovascular adverse events (MACEs) and all cause mortality. Nine thousand, one hundred seventy-six T2DM patients with a mean follow-up of 9.52 ± 1.89 years were included in our study. The mean change in FMI per year was 0.10 ± 0.48 kg/m². The mean change in LBMI per year was 0.05 ± 0.38 kg/m². Normal weight patients had highest FMI/LBMI change; severely obese patients had lowest FMI/LBMI change. A U-shaped relationship was found between the change in FMI/LBMI and all cause mortality. A flat U-shaped relationship was also noted between the change in FMI or LBMI and MACEs. Compared with the second tertile, the first and third tertiles of the change in FMI (HR: 1.18, 95% CI 1.03-1.36; HR: 1.34, 95% CI 1.16-1.54, respectively)/LBMI (HR: 1.24, 95% CI 1.08-1.43; HR: 1.30, 95% CI 1.12-1.50, respectively) had higher all cause mortality; the third tertile of the change in FMI/LBMI showed a marginal increase of MACEs (HR: 1.15, 95% CI 1.01-1.32; HR: 1.17, 95% CI 1.02-1.33, respectively); sensitivity analysis and subgroup analysis showed these associations were not robust. Both lower and larger change in FMI or LBMI are associated with increased all cause mortality compared with the median change among patients with T2DM. Further study is needed to determine whether increased FMI or LBMI increases the risk of MACEs.Trial registration: clinicaltrials.gov., No. NCT00000620.

The Role of Mendelian Randomization Studies in Deciphering the Effect of Obesity on Cancer

Associations of obesity have been established for at least 11 cancer sites in observational studies, though some questions remain as to causality, strength of associations, and timing of associations throughout the life course. In recent years, Mendelian randomization (MR) has provided complementary information to traditional approaches, but the validity requires that the genetic instrumental variables be causally related to cancers only mediated by the exposure. We summarize and evaluate existing evidence from MR studies in comparison with conventional observational studies to provide insights into the complex relationship between obesity and multiple cancers. MR studies further establish the causality of adult obesity with esophageal adenocarcinoma and cancers of the colorectum, endometrium, ovary, kidney, and pancreas, as well as the inverse association of early life obesity with breast cancer. MR studies, which might account for lifelong adiposity, suggest that the associations in observational studies typically based on single measurement may underestimate the magnitude of the association. For lung cancer, MR studies find a positive association with obesity, supporting that the inverse association observed in some conventional observational studies likely reflects reverse causality (loss of lean body mass before diagnosis) and confounding by smoking. However, MR studies have not had sufficient power for gallbladder cancer, gastric cardia cancer, and multiple myeloma. In addition, more MR studies are needed to explore the effect of obesity at different timepoints on postmenopausal breast cancer and aggressive prostate cancer.

Artificial neural network model effectively estimates muscle and fat mass using simple demographic and anthropometric measures

BACKGROUND & AIMS

Lean muscle and fat mass in the human body are important indicators of the risk of cardiovascular and metabolic diseases. Techniques such as dual-energy X-ray absorptiometry (DXA) accurately measure body composition, but they are costly and not easily accessible. Multiple linear regression (MLR) models have been developed to estimate body composition using simple demographic and anthropometric measures instead of expensive techniques, but MLR models do not explore nonlinear interactions between inputs. In this study, we developed simple demographic and anthropometric measure-driven artificial neural network (ANN) models that can estimate lean muscle and fat mass more effectively than MLR models.

METHODS

We extracted the demographic, anthropometric, and body composition measures of 20,137 participants from the National Health and Nutrition Examination Survey conducted between 1999 and 2006. We included 13 demographic and anthropometric measures as inputs for the ANN models and divided the dataset into training and validation sets (70:30 ratio) to build and cross-validate the models that estimate lean muscle and fat mass, which were originally measured using DXA. This process was repeated 100 times by randomly dividing the training and validation sets to eliminate any effect of data division on model performance. We built additional models separately for each sex and ethnicity, older individuals, and people with underlying diseases. The coefficient of determination (R²) and standard error of estimate (SEE) were used to quantify the goodness of fit.

RESULTS

The ANN models yielded high R² values between 0.923 and 0.981. These values were significantly higher than those of the MLR models (p < 0.001) in all cases. The percentage difference in R² between the ANN and MLR models ranged between 0.40% ± 0.02% and 2.65% ± 0.27%. The SEE values of the ANN models, which were below 2 kg for all cases, were significantly lower than those of MLR models (p < 0.001). The percentage difference in SEE values between the ANN and MLR models ranged between -5.67% ± 0.39% and -22.32% ± 1.98%.

CONCLUSIONS

We developed and validated an inexpensive but effective method for estimating body composition using easily obtainable demographic and anthropometric data.

Association of body composition with risk of overall and site-specific cancers: A population-based prospective cohort study

Although excess adiposity has been linked with various cancers, association between body composition and some cancers remains unclear, like lung and prostate cancers. We investigated associations of body composition with risk of overall cancer and major site-specific cancers in a prospective cohort of 454 079 cancer-free participants from UK-Biobank. Body composition was measured with bioimpedance analysis. We evaluated hazard ratio (HR) and 95% confidence interval (CI) with multivariate Cox linear and nonlinear models in men and women separately. We identified 27 794 cancers over 7.6 years of follow-up. Multivariable adjusted models including fat-free mass (FFM) and fat mass (FM) showed that FFM was positively associated with overall cancer risk in men and women (HR 1.03, 95% CI 1.01-1.04 and 1.07, 1.04-1.10, respectively); while the association between FM and overall cancer disappeared after adjusting for FFM. FFM was associated with higher risks of obesity-related cancers combined, stomach (women only), malignant melanoma, postmenopausal breast, corpus uteri, prostate, kidney (men only), and blood cancers and lower risk of lung cancer. FM was associated with higher risks of obesity-related cancers combined, esophageal, colon, lung (men only), postmenopausal breast (at the lower end of FM range), and corpus uteri cancers and lower risks of rectal, malignant melanoma (women only), prostate and blood cancers. FFM and FM seemed to have different effects on cancer risk, and the effects varied substantially by cancer type, in both direction and size. Higher FM/FFM ratio was also associated with some cancers risk, and might be a useful predictor of cancer risk.

Body composition and risk of major gynecologic malignancies: Results from the UK Biobank prospective cohort

OBJECTIVE

To evaluate the association between body composition and subsequent risk of the major gynecologic malignancies.

METHODS

This is a prospective analysis of participants from the UK Biobank. We measured baseline body composition and confirmed cancer diagnosis through linkage to cancer and death registries. We evaluated hazard ratios (HRs) and confidence interval (CIs) with COX models adjusting for potential confounders.

RESULTS

We document 1430 cases of the top three gynecologic malignancies (uterine corpus cancer 847 cases, ovarian cancer 514 cases, and cervical cancer 69 cases) from 245,084 female participants (75,307 were premenopausal and 169,777 were postmenopausal). For premenopausal women, whole body fat-free mass (WBFFM) was associated with an increased risk of uterine corpus cancer (Adjusted HR per unit increase 1.04, 95% CI 1.02-1.06). For postmenopausal women, compared with the first quartile, the fourth quartile of WBFFM and whole body fat mass(WBFM) was associated with 2.16 (95% CI 1.49-3.13) times and 1.89 (95% CI 1.31-2.72) times of increased uterine corpus cancer risk, respectively. Regarding the distribution of body fat mass (FM)/fat-free mass (FFM), FFM distributed in the trunk was associate with increased uterine corpus cancer risk in premenopausal (HR 1.18,95% CI 1.07-1.31) and postmenopausal women (HR 1.13,95% CI 1.09-1.18). Meanwhile, FM/FFM distributed in the limbs present an U-shaped associations with uterine corpus cancer risk. We did not observe any association between aforementioned body composition indices with ovarian or cervical cancer.

CONCLUSION

FM is associated with an increased risk of uterine corpus cancer in postmenopausal women. Meanwhile, FFM is found to be a risk factor for uterine corpus cancer in both premenopausal and postmenopausal women. No association of body composition with ovarian or cervical cancer was observed.

Dietary acid load and lung cancer risk: A case-control study in men

BACKGROUND

Dysregulation of the endogenous acid-base balance can contribute to inflammation and cancer development if metabolic acidosis is sustained. The epidemiologic evidence on the association between diet-dependent acid load and cancer risk is scarce and inconsistent. We aim to explore the possible role of dietary acid load in lung cancer (LC) risk.

METHODS

A case-control study was performed on 843 LC cases and 1466 controls by using a multi-topic questionnaire, including a food frequency questionnaire. Controls were matched to cases by age-frequency, urban/rural residence, and region. Food-derived nutrients were calculated from available databases. The dietary acid load was calculated using validated measures as potential renal acid load (PRAL) score and net endogenous acid production (NEAP) score. Odds ratios (ORs) were estimated by logistic regression.

RESULTS

We found direct associations between dietary acid load and LC risk. The highest quartile of the NEAP score was significantly associated (OR=2.22, p_trend<0.001). The PRAL score displayed similar associations in simpler regression models, but there was no association when a more complex one was used (OR=0.99, p_trend =0.94). The NEAP score was associated with a significant risk increase in all cell types, except for small cell cancers, but the PRAL score did not show any association.

CONCLUSIONS

The NEAP scores, directly associated with meat intake and inversely associated with plant-based foods intake, suggest that a high acid load dietary style may increase LC risk. Studies focused on food groups, and nutritional patterns are in line with our findings. Although the data shown here represent the first one to be published on this issue, further studies are needed to confirm these findings.

The perils of using predicted values in place of observed covariates: an example of predicted values of body composition and mortality risk

BACKGROUND

Several studies have assessed the relation of body composition to health outcomes by using values of fat and lean mass that were not measured but instead were predicted from anthropometric variables such as weight and height. Little research has been done on how substituting predicted values for measured covariates might affect analytic results.

OBJECTIVES

We aimed to explore statistical issues causing bias in analytical studies that use predicted rather than measured values of body composition.

METHODS

We used data from 8014 adults ≥40 y old included in the 1999-2006 US NHANES. We evaluated the relations of predicted total body fat (TF) and predicted total body lean mass (TLM) with all-cause mortality. We then repeated the evaluation using measured body composition variables from DXA. Quintiles and restricted cubic splines allowed flexible modeling of the HRs in unadjusted and multivariable-adjusted Cox regression models.

RESULTS

The patterns of associations between body composition and all-cause mortality depended on whether body composition was defined using predicted values or DXA measurements. The largest differences were observed in multivariable-adjusted models which mutually adjusted for both TF and TLM. For instance, compared with analyses based on DXA measurements, analyses using predicted values for males overestimated the HRs for TF in splines and in quintiles [HRs (95% CIs) for fourth and fifth quintiles compared with first quintile, DXA: 1.22 (0.88, 1.70) and 1.46 (0.99, 2.14); predicted: 1.86 (1.29, 2.67) and 3.24 (2.02, 5.21)].

CONCLUSIONS

It is important for researchers to be aware of the potential pitfalls and limitations inherent in the substitution of predicted values for measured covariates in order to draw proper conclusions from such studies.

Body composition and risk of gastric cancer: A population-based prospective cohort study

The recognition of adiposity as a risk factor for gastric cancer is mainly based on traditional anthropometric indices, such as body mass index, which are unable to discriminate between lean and fat mass. We undertook this study to examine body composition and subsequent risk of gastric cancer. This is a prospective analysis of participants free of cancer from the UK Biobank. We measured baseline body composition with electrical bioimpedance analysis and confirmed cancer diagnosis through linkage to cancer and death registries. We evaluated hazard ratios (HRs) and confidence interval (CIs) with COX models adjusting for potential confounders. We documented 326 cases of cancer from 474,929 participants over a median follow-up of 6.6 years. Both male (HR 1.70, 95% CI 1.01 to 2.89) and female participants (HR 2.47, 95% CI 1.15 to 5.32) in the highest quartile of whole body fat-free mass were associated with increased risk of gastric cancer as compared with those in the lowest quartile.Whole body fat mass was associated with a decreased risk of gastric cancer (HR per 5-unit increase 0.86, 95% CI 0.74 to 0.99) in females, but not in males. We concluded that fat-free mass and fat mass may have different effects on gastric cancer risk. This study provided evidence for individualized weight management for the prevention of gastric cancer.

Adiposity and mortality in Korean adults: a population-based prospective cohort study

BACKGROUND

The Asia-Pacific obesity classification recommends using lower BMI cutoffs in Asians compared with those in Western populations. However, the supporting evidence is scarce and little is known about the exact shape of the relations between adiposity and mortality in Asians.

OBJECTIVES

We investigated the relations of BMI (in kg/m2), waist circumference, and predicted body fat mass with mortality using a population-based prospective cohort of Korean men and women.

METHODS

This analysis included 44,060 Korea National Health and Nutrition Examination Survey 2007-2014 participants who agreed to mortality follow-up through 31 December, 2016. At baseline, height, weight, and waist circumference were measured. Using DXA data, we derived predicted body fat and fat-free mass. Cox proportional hazards models were used to estimate HRs and 95% CIs for the associations with mortality, adjusting for potential confounders. We tested for nonlinearity using the likelihood ratio test comparing nonlinear restricted cubic spline models with linear models.

RESULTS

During ≤9.5 y of follow-up, 1682 deaths were identified. The relations of BMI with all-cause and cardiovascular mortality were J-shaped with the nadir at BMI = 25.0-29.9 (P-nonlinearity < 0.001). Among participants without a history of cancer or cardiovascular disease, waist circumference (≥95 compared with 75.0-79.9 cm: HR: 2.10; 95% CI: 1.54, 2.86) and predicted body fat mass (highest compared with lowest sextiles: 2.55; 95% CI: 1.60, 4.06) were positively associated with all-cause mortality (all P-nonlinearity ≤ 0.03), as well as cancer and cardiovascular mortality. The highest mortality was observed among participants who had both high predicted fat mass and low fat-free mass.

CONCLUSIONS

Our data suggest a strong positive association between adiposity and mortality in a population without pre-existing disease. We observed the lowest mortality at BMI = 25.0-29.9, suggesting that the current cutoff for overweight (BMI ≥23) may require re-evaluation and that BMI alone may not be a useful measure for indicating adiposity in Asians.

Association Between Obesity and Microvascular Diseases in Patients With Type 2 Diabetes Mellitus

This study aimed to evaluate the association between obesity, evaluated by fat mass index (FMI) with the risk of microvascular diseases in patients with type 2 diabetes mellitus (T2DM) and compare the magnitude of associations of FMI, body mass index (BMI), and waist circumference (WC) with the risk of microvascular diseases. We performed a post-hoc analysis of the Action to Control Cardiovascular Risk in Diabetes study. The primary microvascular outcomes of the present study included chronic kidney disease (CKD) progression, retinopathy, and neuropathy. Cox proportional-hazards models were performed to evaluate the association of FMI with microvascular diseases. A discordant analysis was performed to compare the magnitude of associations of FMI, BMI, and WC with the risk of microvascular diseases. Our study included 10,251 T2DM participants with a median of 5 years (interquartile range, 4.2-5.7) of follow-up. A total of 6,184 participants developed CKD progression, 896 participants had retinopathy, and 3,213 participants developed neuropathy (Michigan Neuropathy Screening Instrument, >2.0). After the confounding factors were adjusted for, patients in the highest FMI quartile had a higher risk of CKD progression (HR: 1.26, 95%CI: 1.16-1.36) and neuropathy (HR: 1.93, 95% CI: 1.74-2.15), except for retinopathy (HR: 1.17, 95% CI: 0.96-1.43), than those in the lowest quartile. Discordant analyses found that FMI and WC are better in identifying individuals with obesity-related risk of neuropathy, compared with BMI; neither is better in identifying individuals with obesity-related risk of CKD progression and retinopathy. Obesity is associated with CKD progression and neuropathy in T2DM participants. Further randomized trials are needed to test whether obesity control can improve the outcomes of T2DM participants with CKD or neuropathy. FMI and WC are more useful in identifying obesity-related risk of neuropathy compared with BMI in T2DM patients.

CLINICAL TRIAL REGISTRATION

http://www.clinicaltrials.gov, NCT00000620.

Obesity, Sarcopenia, and Outcomes in Non-Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors and Tyrosine Kinase Inhibitors

Body composition refers to the proportional content of body fat mass and lean body mass that can lead to a continuum of different phenotypes ranging from cachectic/sarcopenic state to obesity. The heterogenetic phenotypes of body composition can contribute to formation of some cancer types and can sometimes lead to disparate outcomes. Both of these extremes of the spectrum exist in patients with non-small cell lung carcinoma (NSCLC). The discovery of new pathways that drive tumorigenesis contributing to cancer progression and resistance have expanded our understanding of cancer biology leading to development of new targeted therapies including tyrosine kinase inhibitors (TKI) and immune checkpoint inhibitors (ICI) that have changed the landscape of NSCLC treatment. However, in the new era of precision medicine, the impact of body composition phenotypes on treatment outcomes and survival is now being elucidated. In this review, we will discuss the emerging evidence of a link between body composition and outcomes in patients with NSCLC treated with TKI and ICI. We will also discuss suggested mechanisms by which body composition can impact tumor behavior and anti-tumor immunological response.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.