T-cell receptors provide potential prognostic signatures for breast cancer

Although T-cell receptors (TCRs) are related to the progression of breast cancer (BC), their prognostic values remain unclear. We downloaded the messenger RNA (mRNA) profiles and corresponding clinical information of 1413 BC patients from the Cancer Genome Atlas and Gene Expression Omnibus database, respectively. The different expression analysis of 104 TCRs in BC samples was performed, and the consensus clustering based on 104 TCRs was performed by using the K-mean method of R language. Univariate cox regression analysis was used to screen TCRs significantly associated with the prognosis of BC, and LASSO Cox analysis was applied to optimize key TCRs. The risk score was calculated using the prognostic model constructed based on six optimal TCRs, and multivariate Cox regression analysis was used to determine whether it was an independent prognostic signature. Finally, the nomogram was constructed to predict the overall survival of BC patients. Six optimal TCRs (ZAP70, GRAP2, NFKBIE, IFNG, NFKBIA, and PAK5), which were favorable for the prognosis of BC patients, were screened. Risk score could reliably predict the prognosis of BC patients as an independent prognostic signature. In addition, when bringing into two independent prognostic signatures, age and risk score, the nomogram model could better predict the overall survival of BC patients. Our results suggested that the poor prognosis of BC patients with high risk might be due to an immunosuppressive microenvironment. In summary, a prognostic risk model based on six TCRs was established and could efficiently predict the prognosis of BC patients.     

Body composition and breast cancer in postmenopausal women: a Danish prospective cohort study

Objective: To assess the importance of body fat mass (BFM) and fat free mass (FFM) for the established positive association between BMI and breast cancer among post‐menopausal women. Research Methods and Procedures: A prospective cohort of 23,788 postmenopausal women included in the Danish study Diet, Cancer, and Health during 1993 to 1997 was linked to the Danish Cancer Registry to identify all cases of breast cancer occurring during 1993 to 2002. Breast cancer incidence rate ratios for anthropometric measurements with adjustment for known risk factors for breast cancer were calculated by Cox regression analyses. Results: Among the most commonly used anthropometric measurements, BMI was positively associated with breast cancer among never users of hormone replacement therapy (HRT). By splitting BMI into two indices, BFM index and FFM index, we found that the incidence rate ratio with each 1 kg/m² among never users of HRT was 0.98 (95% confidence interval, 0.93 to 1.03) for BFM index and 1.12 (95% confidence interval, 1.00 to 1.26) for FFM index after mutual adjustment. Discussion: The finding for BMI was in accordance with previous findings. Our results indicate that the FFM component of BMI may play a role for development of breast cancer among never users of HRT.     

Pre-treatment systemic immune-inflammation index is a useful prognostic indicator in patients with breast cancer undergoing neoadjuvant chemotherapy

The systemic immune-inflammation index (SII = N × P/L) based on neutrophil (N), platelet (P) and lymphocyte (L) counts is used to predict the survival of patients with malignant tumours and can fully reflect the balance between host inflammatory and immune status. This study is conducted to explore the potential prognostic significance of SII in patients with breast cancer undergoing neoadjuvant chemotherapy (NACT). A total of 262 patients with breast cancer received NACT were enrolled in this study. According to the receiver operating characteristic curve, the optimal cut-off value of SII was divided into two groups: low SII group (<602 × 10⁹/L) and high SII group (≥602 × 10⁹/L). The associations between breast cancer and clinicopathological variables by SII were determined by chi-squared test or Fisher's exact test. The Kaplan-Meier plots and log-rank test were used to determine clinical outcomes of disease-free survival (DFS) and overall survival (OS). The prognostic value of SII was analysed by univariate and multivariate Cox proportional hazards regression models. The toxicity of NACT was accessed by National Cancer Institute Common Toxicity Criteria (NCICTC). According to univariate and multivariate Cox regression survival analyses, the results showed that the value of SII had prognostic significance for DFS and OS. The patients with low SII value had longer DFS and OS than those with high SII value (31.11 vs 40.76 months, HR: 1.075, 95% CI: 0.718-1.610, P = .006; 44.47 vs 53.68 months, HR: 1.051, 95% CI: 0.707-1.564, P = .005, respectively). The incidence of DFS and OS in breast cancer patients with low SII value was higher than that in those patients with high SII value in 3-, 5- and 10-year rates. The common toxicities after NACT were haematological and gastrointestinal reaction, and there were no differences by SII for the assessment of side effects of neoadjuvant chemotherapy. Meanwhile, the results also proved that breast cancer patients with low SII value and high Miller and Payne grade (MPG) survived longer than those breast cancer with high SII value and low MPG grade. In patients without lymph vessel invasion, these breast cancer patients with low SII value had better prognosis and lower recurrence rates than those with high SII value. Pre-treatment SII with the advantage of reproducible, convenient and non-invasive was a useful prognostic indicator for breast cancer patients undergoing neoadjuvant chemotherapy and is a promising biomarker for breast cancer on treatment strategy decisions.     

Brief communication: Body mass index,body adiposity index,and percent body fat in asians

Human obesity is a growing epidemic throughout the world. Body mass index (BMI) is commonly used as a good indicator of obesity. Body adiposity index (BAI = hip circumference (cm)/stature (m)^1.5 ? 18), as a new surrogate measure, has been proposed recently as an alternative to BMI. This study, for the first time, compares BMI and BAI for predicting percent body fat (PBF; estimated from skinfolds) in a sample of 302 Buryat adults (148 men and 154 women) living in China. The BMI and BAI were strongly correlated with PBF in both men and women. The correlation coefficient between BMI and PBF was higher than that between BAI and PBF for both sexes. For the linear regression analysis, BMI better predicted PBF in both men and women; the variation around the regression lines for each sex was greater for BAI comparisons. For the receiver operating characteristic (ROC) analysis, the area under the ROC curve for BMI was higher than that for BAI for each sex, which suggests that the discriminatory capacity of the BMI is higher than the one of BAI. Taken together, we conclude that BMI is a more reliable indicator of PBF derived from skinfold thickness in adult Buryats. Am J Phys Anthropol 152:294–299, 2013. © 2013 Wiley Periodicals, Inc.     

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C-based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan-Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C-based nomogram to predict the OS, verified by the time-dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low- and high-nomogram score groups. Our results indicated that low-CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5-year OS was superior to the tumour-node-metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low-risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.     

Prognostic value of prognostic nutritional index and systemic immune-inflammation index in patients with osteosarcoma

The aim of this study was to evaluate the relationship between prognostic nutritional index (PNI) and systemic immune-inflammation index (SII) and clinical features and prognosis of osteosarcoma patients. We retrospectively investigated 126 patients with surgery for osteosarcoma between 2012 and 2018 at our hospital. The preoperative PNI was calculated as albumin level (g/L) + 5 × total lymphocyte count (10 ⁹/L). The SII was defined as platelet × neutrophil/lymphocyte counts. The optimal cut-off values for PNI and SII were evaluated with receiver operating curve analysis. Clinical features and PNI and SII were tested with the χ ² test. The effects of PNI and SII on overall survival (OS) was investigated by Kaplan–Meier method and Cox proportional hazards model. A low preoperative PNI was remarkably correlated with tumor size, Enneking stage, pathological fracture, local recurrence, metastasis, and neoadjuvant chemotherapy ( p < 0.05). Whereas, a high SII was significantly associated with tumor size, histological type, Enneking stage, and neoadjuvant chemotherapy ( p < 0.05). There was a significant negative relationship between the PNI and SII ( r = 0.384; p < 0.001). For univariate analyses, the results revealed that tumor size, local recurrence, metastasis, PNI, and SII were predictors of OS ( p < 0.05). In multivariate analyses, local recurrence ( p = 0.010), metastasis ( p < 0.001), PNI ( p < 0.001), and SII ( p = 0.029) as independent prognostic factors were significantly correlated with OS. This study suggested that PNI and SII could be important prognostic parameters for patients with osteosarcoma.     

The robustness of dual isocenter VMAT radiation therapy for bilateral lymph node positive breast cancer

PurposeTo investigate the use of dual isocenters for VMAT planning in patients with lymph node positive synchronous bilateral breast cancer (BBC) compared to a single isocenter option.MethodsTreatment plans of 11 patients with lymph node positive BBC were retrospectively analyzed using two different VMAT planning techniques: dual-isocenter split-arc VMAT plans (Iso2) were compared with mono-isocenter VMAT plans (Iso1). For Iso2 plans, PTV dose was investigated after introducing ±2 and ±5 mm couch shift errors between the two isocenters in the lateral, longitudinal and vertical direction.ResultsFor both techniques the planning aims for PTV coverage and OARs were met. The mean dose for the bilateral lungs and heart was reduced from 11.3 Gy and 3.8 Gy to 10.9 Gy (p < .05) and 3.6 Gy (p < .05), respectively, for Iso2 plans when compared to Iso1 plans. Positive statistically significant correlation (rho = 0.76, p = .006) was found between PTV volume and D2cc_PTV for Iso1 plans. No clinically significant change was seen in the D98_CTV or D2cc_PTV after the 2 and 5 mm errors were introduced between isocenters for Iso2 plans.ConclusionsThe split arc method was shown to be a feasible treatment technique in the case of synchronous BBC for both mono and dual isocenter techniques. The dose parameters were slightly favoring dual-isocenter option instead of mono-isocenter. The dual-isocenter method was shown to be a robust treatment option in the presence of ≤5 mm errors in the shifts between the two isocenters.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Development and validation of stable ferroptosis- and pyroptosis-related signatures in predicting prognosis and immune status in breast cancer

To develop and validate the predictive effects of stable ferroptosis- and pyroptosis-related features on the prognosis and immune status of breast cancer (BC). We retrieved as well as downloaded ferroptosis- and pyroptosis-related genes from the FerrDb and GeneCards databases. The minimum absolute contraction and selection operator (LASSO) algorithm in The Cancer Genome Atlas (TCGA) was used to construct a prognostic classifier combining the above two types of prognostic genes with differential expression, and the Integrated Gene Expression (GEO) dataset was used for validation. Seventeen genes presented a close association with BC prognosis. Thirteen key prognostic genes with prognostic value were considered to construct a new expression signature for classifying patients with BC into high- and low-risk groups. Kaplan–Meier analysis revealed a worse prognosis in the high-risk group. The receiver operating characteristic (ROC) curve and multivariate Cox regression analysis identified its predictive and independent features. Immune profile analysis showed that immunosuppressive cells were upregulated in the high-risk group, and this risk model was related to immunosuppressive molecules. We successfully constructed combined features of ferroptosis and pyroptosis in BC that are closely related to prognosis, clinicopathological and immune features, chemotherapy efficacy and immunosuppressive molecules. However, further experimental studies are required to verify these findings.     

Convergence and divergence of genetic and modular networks between diabetes and breast cancer

Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi‐dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM‐related genes, 386 type 2 diabetes (T2DM) genes and 873 BC‐related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non‐overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long‐term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases.     

NK cell and IFN signatures are positive prognostic biomarkers for resectable pancreatic cancer

To establish prognostic biomarkers and to identify potential novel therapeutic targets, we performed integrative immunomonitoring of blood and tumor in patients with resectable pancreatic cancer. Flow cytometry (FC) was employed for phenotyping immune cells, multiplex bead assays for plasma cytokine and chemokine determination, and RNA-Seq for the analysis of gene expression in the tumor. Nineteen pancreatic cancer patients were stratified into those with longer or shorter than median recurrence-free survival after surgery (median, 426 days). There were no significant differences between the two groups for clinical parameters including age, sex, surgical procedure, stage, or postoperative adjuvant therapy. However, we found that the percentages of NK cells as assessed by FC in peripheral blood mononuclear cells were higher in patients with late recurrence (P?=?.037). RNA-Seq data indicated no differences in the amount of immune cells or stromal cells between the two groups, although NK cells in the tumor did tend to be higher in patients with late recurrence (P?=?.058). Type I and II IFN signatures were enriched in late-recurring tumors (FDR q-value <0.001), while genes related to KRAS signaling and the epithelial mesenchymal transition (EMT) were enriched in early recurrence. We conclude that tumor-intrinsic properties of metastasis and recurrence influence prognosis, whereas NK cells that might contribute to prevent metastasis are associated with longer recurrence-free survival. Therefore, enhancement of NK cell activity and inhibition of the EMT and KRAS signaling might represent appropriate therapeutic targets following surgical resection of pancreatic cancer.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

Evaluation of body mass index as a prognostic indicator from two rough‐toothed dolphin (Steno bredanensis) mass strandings in Florida

Rough‐toothed dolphins (Steno bredanensis) are a common mass stranding species in Florida. These large stranding events typically include a small number of sick or injured individuals and a much larger number of healthy individuals, making rapid triage essential. Little data exist on rehabilitation outcomes, and historically, successful outcomes are limited. Furthermore, very little data exist on the feeding habits and dietary needs of this species. This study compared morphology and body mass index (BMI) in two rough‐toothed dolphin mass stranding events in Florida: August 2004 (n = 36) and March 2005 (n = 32). The two groups were significantly different in morphologic measurements, with age and gender‐adjusted intake BMI significantly (p < .01) different (2004 = 0.34 ± 0.02; 2005 = 0.41 ± 0.02) between groups. Ten animals from 2005 had weights tracked throughout the rehabilitation process and demonstrated an initial drop in BMI followed by an increase and a plateau prior to release. When comparing initial BMI by stranding outcome, individuals that were rehabilitated and released had a significantly (p = .03) higher BMI than individuals who were euthanized. However, there was no difference between dolphins that died of natural causes (p = .56) and animals successfully rehabilitated. Analysis of BMI can be a useful marker in triage during a stranding, when resources are limited to identify individuals most likely to survive, as well as in determining the appropriate body condition for release. The data reported here can provide guidance on evaluating the nutritive status on this uncommon species that would otherwise be difficult to obtain among wild populations.     

Luminal (Her2 negative) prognostic index and survival of breast cancer patients

PurposeThe group of luminal (Her2 negative) is distinguished from other subtypes of breast cancer. We aimed to produce a prognostic index specific for luminal (Her2 negative) subtype breast cancer that could assist clinical treatment.MethodsThe test set comprised 406 consecutive luminal (Her2 negative) breast cancer patients. The relationship of 11 clinicopathologic factors including survivin with the 5-year disease-free survival was analyzed.ResultsIn univariate analysis, TNM stage, surgery, tumor size, lymph node involvement, and survivin expression were prognostic factors. In multivariate analysis, tumor size [HR (95% CI): 1.98 (1.12–3.49), p = 0.019], the number of lymph node metastasis [HR (95% CI): 1.75 (1.33–2.29), p < 0.0001] and the expression of progesterone receptor [HR (95% CI): 0.58 (0.36–0.95), p = 0.029] can independently predict prognosis. Prognostic index (PI) was calculated as 0.68 × tumor size + 0.56 × the number of lymph node metastasis − 0.54 × PR. According to the PI, patients were categorized into three groups: low, middle, and high risk group with the 5-year disease-free survival rates of 91.91%, 84.97% and 70.47%, respectively (P < 0.001). In the validation set, the luminal prognostic index (LPI) remained significant.ConclusionThe LPI may be a useful tool for evaluating the outcome of patients with luminal (Her-2 negative) breast cancer.     

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.     

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.     

Body mass index,height and early-onset basal cell carcinoma in a case-control study

IntroductionBasal cell carcinoma (BCC) is the most common malignancy in the US. Body mass index (BMI) and height have been associated with a variety of cancer types, yet the evidence regarding BCC is limited. Therefore, we evaluated BMI and height in relation to early-onset BCC (under age 40) and explored the potential role of ultraviolet (UV) radiation exposure and estrogen-related exposures in the BMI-BCC relationship.MethodsBCC cases (n = 377) were identified through a central dermatopathology facility in Connecticut. Control subjects (n = 389) with benign skin conditions were randomly sampled from the same database and frequency matched to cases on age (median = 36, interquartile range 33–39), gender, and biopsy site. Participants reported weight (usual adult and at age 18), adult height, sociodemographic, phenotypic, and medical characteristics, and prior UV exposures. We calculated multivariate odds ratios (ORs) and 95% confidence intervals (CIs) using unconditional logistic regression models.ResultsAdult BMI was inversely associated with early-onset BCC (obese vs. normal OR = 0.43, 95% CI = 0.26–0.71). A similar inverse association was present for BMI at age 18 (OR = 0.54, 95% CI = 0.34–0.85). Excluding UV exposures from the BMI models and including estrogen-related exposures among women only did not alter the association between BMI and BCC, indicating limited mediation or confounding. We did not observe an association between adult height and BCC (OR per cm = 1.00, 95% CI = 0.98–1.02).ConclusionsWe found a significant inverse association between BMI and early-onset BCC, but no association between height and BCC. This association was not explained by UV exposures or estrogen-related exposures in women.     

Body mass index and employment status: A new look

Earlier literature has usually modelled the impact of obesity on employment status as a binary choice (employed, yes/no). I provide new evidence on the impact of obesity on employment status by treating the dependent variable as a as a multinomial choice variable. Using data from a representative English survey, with measured height and weight on parents and children, I define employment status as one of four: working; looking for paid work; permanently not working due to disability; and, looking after home or family. I use a multinomial logit model controlling for a set of covariates. I also run instrumental variable models, instrumenting for Body Mass Index (BMI) based on genetic variation in weight. I find that BMI and obesity significantly increase the probability of “not working due to disability”. The results for the other employment outcomes are less clear. My findings also indicate that BMI affects employment through its effect on health. Factors other than health may be less important in explaining the impact of BMI/obesity on employment.     

Body mass index and relative handgrip strength are associated with the prevalence of hypertension in Korean elderly: Korean national fitness assessment in 2019

[Purpose]This study aimed to analyze the prevalence of hypertension according to the body mass index (BMI) and relative handgrip strength (RHGS) among elderly individuals in Korea. [Methods]We analyzed the data of 44,183 Korean elderly individuals over 65 years old (men: n = 15,798, age = 73.31 ± 5.04 years, women: n = 28,385, age = 72.14 ± 5.04 years) obtained from the Korean National Fitness Assessment in 2019. All the participants were categorized into three groups according to the BMI and RHGS; additionally, one-way ANOVA and logistic regression analysis were performed. [Results]Overweight (men: 1.16 odds ratio [OR] 1.06–1.26, 95% confidence interval [CI]; women: 1.15 OR, 1.07–1.23 95% CI) and obese (men: 1.54 OR, 1.42–1.66 95% CI; women: 1.44 OR, 1.36–1.53 95% CI) elderly individuals showed a higher prevalence of hypertension than elderly individuals with normal weight, after controlling for age. In men, a lower RHGS was associated with a higher prevalence of hypertension after controlling for age (weak RHGS: 1.09 OR, 1.00–1.17 95% CI; middle RHGS: 1.21 OR, 1.12–1.31 95% CI vs. strong RHGS). [Conclusion]A higher BMI was associated with the prevalence of hypertension in the elderly Korean population. In addition, a lower RHGS was associated with the prevalence of hypertension in elderly Korean men.