Thirteen million cancer deaths and 21.7 million new cancer cases are expected in the world by 2030.Breast cancer is considered as the main cause of cancer mortality in women aged 20-59 years. microRNAs (miRNAs)regulate gene expression at the post-transcriptional level and they are highly expressed in malignancies, includingbreast cancer. The role of miRNAs in the pathogenesis of breast cancer is not fully understood. In the present study,for the first time, the impact of hsa-miR-423 rs6505162 genotypewas analyzed. This case-control study was conducted on 153 clinicopathological proven breast cancerpatients and 153 sex-matched healthy women with no history of any cancer type and relative patients. The patientsand controls were genotyped and association of their clinical characteristics with hsa-miR-423 rs6505162 was associated with the increased risk ofbreast cancer . The findings indicated that CC genotype of hsa-miR-423 rs6505162 could be considered as a novel risk factor in breastcancer pathogenesis in Isfahan province of Iran.The data suggested that  The rate of cancers is increasing day by day in theworldwide. Approximately, 30000 Iranians lose their lifedue to cancer each year . Breast microRNAs (miRNAs) are an abundant class of ancient,noncoding and small single-stranded molecules (20-22 nucleotides) that participate in transcriptional andtranslational regulations of their target genes . SincemIn modern genetics, phenotypic variations for traitswith medical importance are of special interests. Somevariations are due to SNP in miRNAs, named as MirSNPs,which represent a new class of potential biomarkers.These markers have attracted increasing interests due totheir potential role in the development of various typesof cancer . In factVarious reports from the studies performed on anumber of populations with breast cancer patients havedemonstrated that SNPs in the precursor of miRNAs(pre-miRNAs), exclusively miR-423 A/C polymorphism,could affect maturation or expression of the respectivemature miRNA -20.hsa-mirR-423 rs6505162variants on breast cancer risk in the Iranian population. Therefore, the current study wascarried out to find the possible association between hsa-mirR-423 rs6505162variants polymorphisms and susceptibility to breast cancer in the Isfahan central provinceof Iran.To date, there is no report on the impact of This study was qualified by meeting the followingcriteria: i. Designed as case-control, ii. Assessedthe association between Hsa-miR-423 rs6505162polymorphism and breast cancer risk, iii. Providedadequate numbers of genotype distribution in case andcontrol groups, in order to calculate the odd ratios (OR)and its 95% confidence interval (CI).In the present case-control study, 153 geneticallyunrelatedfemales with breast cancer from the OmidHospital  were investigated between2011 and 2014. As control, the same numbers ofhealthy independent females were included in thestudy. The study design and recruitment procedureswere described previously . The prohsa-miR-423 rs6505162 polymorphism was selected by considering thefollowing criteria: i. the minor allele frequency (MAF) of selected SNP was < 5%(0.4978), ii. It was functional  software (two by twotables available from http://www.quantitativeskills.com/sisa/). The genotypic associations wereexamined using SNPstats, (http://bioinfo.iconcologia.net/SNPstats_web). The results were consideredstatistically significant at P≤0.05.The Pearson’s chi-square χ2 test was used to evaluatethe Hardy-Weinberg equilibrium by consideringstatistical significance at a P≥0.05 . PopulatThe patterns of inheritance were determined by Akaikeinformation criterion (AIC) and Bayesian informationcriterion (BIC) .Clinical and pathological characteristics of the breastancer patients were summarized in table 2.hsa-miR-423 rs6505162 in 153 breast cancerpatients  products from ARMS-PCR reactions were shown in Figure 1. Data showed that thePearson Chi-Square significance value was 0.00071 (P<0.05). Analysis of theassociation of genotyping data with the risk of breast cancer using SNPstats, showed a clearassociation of hsa-miR-423 rs6505162 in codominance, recessive andLog-additive patterns with breast cancer. These data showed the presence of possiblerelationship between hsa-miR-423 rs6505162 and the incidence of breastcancer. The genotyping data also showed that the CC genotype was associated with theincreased risk of breast cancer in codominance , recessive  and Log-additive pattern of inheritance. This indicated that thehsa-miRw rs6505162 C allele in comparison with the A allele couldincrease the risk of breast cancer . Based on the AICand BIC factors, the best fitting inheritance pattern would be a recessive model , dominance , over dominance  and Log-additive mode ofinheritance . In view of the AIC and BIC factors, thebest fitting model would be dominant inheritance model , dominance  and Log-additive pattern . The AIC and BICindicated the presence of dominant inheritance model as the best fitting model . Analysis of Hardy-Weinberg Equilibrium showed the presence of equilibrium for thismarker in the Isfahan population (P=0.1627). Analysis of the allele frequency andheterozygosity for the indicated marker using GENEPOP website illustrated 0.356 MAF and41.18% heterozygosity rate for hsa-miR-423 rs6505162 (data not shown). Theestimated PIC value was 0.3534 by determining the frequency of alleles per locus through PICcalculator website.Moreover, as shown in Table 5, hsa-miR-423 rs6505162 polymorphism wasinvestigated on 306 breast cancer cases and control individuals in the Isfahan population. The results indicated that hsa-mirR-423rs6505162 polymorphism was associated with susceptibility to increased risk of breastcancer. In this regard, recently genetic susceptibility due to SNP has been one of the majorfocuses of cancer molecular biology research  gene, which is involved in alternative splicing of mRNAs. Itspre-miRNA can produce two mature transcripts, miR-423-3p andmiR-423- 5p, that are at the 3´- and 5´-terminus of thepre-miR-423, respectively .To date, a large number of investigations have been performed to unravel the exact role ofMirSNPs in precursor and mature miRNA and their influences on cancer susceptibility ,32. miRectively . The rs6miR-423 has focused on expressionanalyses and showed that abnormal expression of the miRNA could affect different types ofcancer during cellular differentiation  allele in patients who were >50 years old had 2-3 folds higher risk of developingbreast cancer. Moreover, 2-4.5 folds increase in the risk of developing breast cancer wasobserved in those with invasive stages of breast cancer progression compared to those at thesuperficial stages.On the other hand, another study showed that e cancer . Intereshsa-miR-423 rs6505162 polymorphism with increasedrisk of breast cancer, its allele frequency in control population was investigated. The datashowed presence of Hardy-Weinberg equilibrium for hsa-miR-423 rs6505162marker in the Isfahan population. The data indicated a high heterozygosity forhsa-miR-423 rs6505162 variant (with He=41.18%). Moreover, analysis showedPIC=0.3534. This indicated diversity of hsamiR- 423 rs6505162 in the Isfahan population. Inthe present study, MAF was compared to the other population based on the 1000 genome projectdatabase (data not shown). Data showed that the highest MAF was related to Chinesepopulation (0.8127) indicating a big difference with the Iranian population. The lowest MAFrefers to the American population (0.2541).Given the association of hsa-miR-423 rs6505162 polymorphism.All together, the data from this study showed that there was an association betweenhsa-miR-423 rs6505162 A/C polymorphism and susceptibility to breastcancer in the population of Isfahan central province of Iran. We highly suggest usinganti-sense RNA of hsa-miR-423 rs6505162 in animal model to evaluate thispoint.On the other hand, the British population has the closest distance to the Iranianpopulation for both A and C alleles of hsa-miR-423 rs6505162 as a newmolecular marker in molecular cancer diagnosis and prevention as well as the development ofpossible individually tailored miRNA-based therapy. Furthermore, these data may indicatethat polymorphism in pre-microRNA (hsa-miR-423 rs6505162) could play a rolein the pathogenesis of breast cancer.Data obtained from this study could suggest


Growing data from epidemiological studies highlight the association between excess body fat and cancer incidence, but good indicative evidence demonstrates that intentional weight loss, as well as increasing physical activity, offers much promise as a cost-effective approach for reducing the cancer burden. However, clear gaps remain in our understanding of how changes in body fat or levels of physical activity are mechanistically linked to cancer, and the magnitude of their impact on cancer risk. It is important to investigate the causal link between programmes that successfully achieve short-term modest weight loss followed by weight-loss maintenance and cancer incidence. The longer-term impact of weight loss and duration of overweight and obesity on risk reduction also need to be fully considered in trial design. These gaps in knowledge need to be urgently addressed to expedite the development and implementation of future cancer-control strategies. Comprehensive approaches to trial design, Mendelian randomisation studies and data-linkage opportunities offer real possibilities to tackle current research gaps. In this paper, we set out the case for why non-pharmacological weight-management trials are urgently needed to support cancer-risk reduction and help control the growing global burden of cancer. According to the World Health Organisation (WHO), 1.9 billion adults and over 340 million children and adolescents were living with overweight or obesity in 2016 (i.e. a body mass index (BMI) > 25 kg/m2) and these numbers are projected to rise.3 This situation is compounded by global physical activity data suggesting that more than a quarter of the world’s population is insufficiently active.4 Furthermore, overweight and obesity are occurring at earlier ages,3 thereby increasing lifetime exposure to associated risks. Current estimates suggest that overweight and obesity could overtake smoking as the single biggest cause of cancer in UK women in around 25 years5 and this premise is also echoed in international reports.6 Of all new global cancer cases in 2012, 481,000 (or 3.6%) were considered to be attributable to excess body mass index (BMI).7Cancer causes one in six deaths globally and is now overtaking cardiovascular disease as the leading cause of death across much of the world.The substantial reduction in lung cancer incidence in countries where public health initiatives have brought about a significant decrease in smoking indicates the potential of primary cancer prevention by societal interventions. The implementation of equitable, population-wide programmes for obesity prevention and management is eagerly awaited, but sufficient evidence already currently exists to justify a research focus on intentional weight loss and cancer-risk-reduction trials. The ultimate objective of trials with positive results must be to create further leverage for the development and implementation of policies aimed at improving the health of the general public—not just the individuals who have the resources and motivation to participate in individually focussed weight-loss programmes.9 and statins,10 in cancer prevention, the evidence is much weaker. The effectiveness of these pharmaceuticals is relatively modest compared with drugs available for treating cardiovascular risk factors . In addition, the mechanisms of action of these potential cancer-preventive agents are not well-established, and their pleiotropic and undesirable side effects must be considered11 alongside evidence of inverse associations with mortality.12Pharmaceutical options are available to reduce the risk of obesity-related diabetes and heart disease, but the portfolio of agents that reduce the risk of developing cancer is very limited. Considerable amounts of data, including evidence from randomised controlled trials, support the role of aspirin and tamoxifen in reducing colorectal cancer and breast cancer risk, respectively, and, although further studies also support a role for other drugs, such as metformin13 it is difficult to predict how long it will take to identify effective drugs with low risk of side effects, and we cannot afford to wait for pharmacological approaches alone to prevent cancer risk. The benefit to potentially affected individuals and their families and the direct and indirect economic implications of cancer-risk reduction are far-reaching. Addressing cancer prevention beyond pharmacological solutions has therefore become a global imperative, and strategies that offer disease reduction should no longer be ignored. We now have the evidence to demonstrate that intentional weight loss and weight management as well as increasing physical activity offer much promise as cost-effective approaches for reducing the risk of developing cancer.Based on the disappointing results of a number of cancer chemoprevention trials conducted over the past three decades,14 As population rates of overweight and obesity continue to rise, so will the incidence of common cancers linked to excess body fat (EBF). As a consequence, escalating costs attributable to future cancer treatments and the long-term clinical management of associated comorbidities will place an unrelenting economic burden on healthcare systems. Action needs to be taken now, otherwise our failure to seriously address this topic will leave a sad legacy for the next generation.The association between obesity and cancer has been reported and discussed in the literature since the early part of the 20th century.15 reported associations between body fatness at a young age and the development in later life of eight types of cancer. Jensen et al.16 subsequently reported from the Copenhagen School Health Records Registry that children who were heavier or gaining more weight than average at 7–13 years of age  had a significantly greater risk of adult colon cancer.There is a strong need to address the role of EBF in early life, as it has been demonstrated to influence the risk of many diseases, including cancer, in adulthood. Hidayat et al.17), which is probably most relevant in women with a body mass index (BMI) < 23.4 kg/m2 at age 20 (who are more likely to gain weight in adulthood than women with a BMI > 23.4 kg/m2).18In adulthood, it seems that although the link between obesity and cancer is becoming more apparent, the significance of weight gain across adult life remains largely ignored. Not only is weight gain the pathway to overweight and obesity, but it is also an independent risk factor for postmenopausal breast cancer risk , stomach (cardia), pancreas, gall bladder, liver, colorectum, breast , ovary, endometrium, prostate (advanced) and kidney. In addition, a WHO International Agency for Research on Cancer (IARC) Working Group found evidence relating EBF to meningioma, thyroid cancer and multiple myeloma,19 and a hospital-based Danish study of 313,221 patients reported overweight and obesity being related to haematological and neurological cancers.20 The reported inverse associations between physical activity and the risk of cancer at 13 sites, including some of the most common cancers 22 reflect the important role of a physically active lifestyle in cancer prevention, either via direct mechanisms, such as improved metabolic control or via its role in the prevention of adult weight gain.23 Furthermore, studies show that structured exercise in combination with support for dietary-led weight loss induces more weight loss than exercise or diet alone, and has the greatest impact on blood-borne biomarkers associated with common cancers, including insulin resistance and circulating levels of sex hormones, leptin and inflammatory markers.28The latest (2018) World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) expert report29 MR is an instrumental variable method to appraise causality within observational epidemiology, utilising germline genetic variants that are robustly associated with potentially modifiable exposures as proxies  for the risk factor of interest. As germline genetic variants tend to be randomly distributed with respect to most human traits in the general population, MR studies are less likely to be affected by the sorts of confounding factors that typically bias observational findings. Additionally, as germline genotypes cannot be affected by the presence of disease, the generation of spurious results through reverse causation is avoided. The objective is to identify modifiable intervention targets  on the intermediate causal pathway between genetic factors and disease. DNA, although itself unmodifiable, operates through modifiable pathways, e.g., the proprotein convertase subtilisin/kexin type 1 (PCSK1) gene regulates insulin synthesis; fat mass- and obesity-associated (FTO) gene promotes food intake. MR exploits this to identify modifiable exposures that can be used for disease prevention and therapeutic strategies.In the absence of randomised clinical trials, evidence for causality can be strengthened by Mendelian randomisation (MR) studies.33 Indeed, MR analysis suggests that the obesity-related cancer burden has been substantially underestimated.34 The volume and location of fat tissue are strong determinants of insulin resistance and dyslipidaemia, and MR studies support strong effects of higher BMI on higher fasting levels of insulin, glucose, triglycerides, remnant cholesterol and lower high-density lipoprotein (HDL) cholesterol.35 The adverse metabolic effects of higher fatness are already evident in late childhood and might worsen with longer time exposure.36 Higher body fatness also raises systolic and diastolic blood pressure, and impairs immunity via its association with elevated pro-inflammatory factors such as interleukin-6.37 Several of these metabolic traits are associated with an increased risk of obesity-related cancers, with MR evidence being the strongest for higher fasting insulin.38Studies using MR support the influence of higher body fatness on greater risk of oesophageal, gastric, pancreatic, renal, colorectal, endometrial and ovarian cancers.39 suggesting a long-term protective effect of EBF on breast cancer risk later in life. Analysis from the cohort-pooling project papers40 on premenopausal breast cancer confirms that relative overweight at age 18–24 is associated with a modest reduction in the risk of premenopausal breast cancer up to the age of ~50 years, and additional analyses41 indicate that weight gain from ages 18–24 to 35–44 or to 45–54 years is also inversely associated with breast cancer overall  and with oestrogen-receptor(ER)-positive breast cancer .It is important to note that, from a life-course perspective, higher body fatness in childhood and adolescence is inversely related to the risk of premenopausal breast cancer as well as postmenopausal breast cancer,42 These findings suggest that the effect of early-life body size might persist into later life, regardless of interventions to influence adult body size. There is also evidence18 that early-life body size exerts a protective effect even when accounting for age at menarche. A better understanding of the mechanisms linking childhood body size and timing of puberty with later breast cancer risk could help inform potential interventions.Evidence related to MR studies also indicates that a genetically predicted larger body size at age 10 might protect against breast cancer in women independent of subsequent body size at a mean age of 56.5 years.17 However, the inverse association of obesity with premenopausal breast cancer does not alter the overall harmful effects of obesity, given that weight and weight gain are positively associated with risks of postmenopausal breast cancer, several other types of cancer and other adverse health outcomes. In addition, women with obesity or who have obesity diagnosed with breast cancer are more likely to have poorer outcomes than leaner women .43Understanding the crossover effect of obesity with risk reduction before, and risk increase after, menopause is poorly characterised, and further work aimed at understanding the biological mechanisms of how obesity, weight gain and weight change all impact on breast cancer risk is needed.44 This association was the strongest among women with obesity or who had obesity at baseline. In this cohort, a lower risk of breast cancer among women who lost weight compared with women whose weight remained stable was also reported.45 Similarly, the 17-year follow-up of the UK Women’s Cohort Study has shown a lower risk of postmenopausal breast cancer in those individuals who lost weight compared to women with stable weight or those who gained weight.46Until 2010, the evidence that intentional weight loss in adulthood modifies cancer risk was sparse, and mostly relied on self-reported body weight with relatively short follow-up periods. However, long-term follow-up data from the Women’s Health Initiative cohort have since reported that, after a mean follow-up of 11.4 years, women with modest weight loss  had a lower risk of endometrial cancer compared with those who did not lose weight.47 which assessed data from 180,885 women aged ≥50 years in whom 6930 invasive breast cancers were identified at the final follow-up. All women were surveyed at three points  and final follow-up (10 years)). Sustained weight loss was defined as no less than 2 kg lost between baseline and the first follow-up, which was not regained by the final follow-up. The results demonstrated that, compared with women with stable weight, women with sustained weight loss had a lower risk of breast cancer than women whose weight remained stable; moreover, the larger the weight loss, the lower the risk. It is notable that even modest weight loss (2–4.5 kg) was associated with a significant reduction in risk . Risk reduction was specific to women not using postmenopausal hormone-replacement therapy and the lowest risk was for women who sustained at least 9 kg of weight loss (who were not taking hormone therapy).The largest study to date on weight change and postmenopausal breast cancer is from the Pooling Project of Prospective Studies of Diet and Cancer (DCPP),48 The cancer-protective effect of bariatric surgery seems to be more pronounced in women than in men, and most marked for a reduction in breast cancer risk. It is notable that the favourable impact of bariatric surgery on cancer risk for adults in mid- and later life occurs within a relatively short follow-up period and is independent of physical activity. However, people undergoing bariatric surgery do not necessarily reflect the general overweight and obese population, and the physiological response following acute weight loss might in itself produce effects that might not be matched by weight loss induced through lifestyle interventions.49 A systematic review of weight-loss trials50 reported a significant reduction in the risk of all-cause mortality, cardiovascular mortality and cancer mortality. Furthermore, in 2020, the Look Ahead Research Group reported51 that an intensive lifestyle-intervention trial of 5145 participants, which targeted weight loss, successfully lowered the incidence of obesity‐related cancers by 16% in adults with overweight or obesity and type 2 diabetes after a median follow-up of 11 years, highlighting the potential success of such interventions in cancer-risk reduction.Evidence for the impact of weight loss on cancer-risk reduction is also emerging from intervention studies, although no study has yet been designed (in terms of size and follow-up period) specifically to assess the effects of weight loss on cancer incidence or mortality in the general population. Several studies have evaluated the effect of bariatric surgery on cancer risk, comparing people with obesity who underwent surgery with that of individuals in an obesity (non-randomised) control group who did not. According to a systematic review, bariatric surgery was reported to be associated with a reduction in the incidence of overall cancer (pooled odds ratio (POR) = 0.72: 95% CI 0.59–0.87) and in the incidence of obesity-related cancers (POR = 0.55: 95% CI 0.31–0.96).53 and it is particularly notable that in a 15-year follow-up of the Diabetes Prevention Programme, the incidence of diabetes still remained lower—by 27%—in the lifestyle-intervention group compared with the placebo group.54Irrespective of the mode of weight loss, it is important to investigate whether or not programmes that successfully achieve short-term modest weight loss followed by weight-loss maintenance confer benefit on cancer incidence. The potential effect of the latency of risk reduction following weight loss, as well as the duration of overweight and obesity, need to be fully considered in trial design. Furthermore, it is important to identify whether or not the benefits of weight loss are offset by any subsequent regain in weight. There is much to be learnt from highly successful diabetes- prevention programmes based on change in caloric intake and increased physical activity for weight loss,55 the latter might be particularly important in weight-loss maintenance.56 However, it is likely that physical activity confers additional benefits on the reduction of cancer risk, for example, through modulation of immune-regulatory pathways,57 reduced oxidative stress,58 epigenetic changes59 and reduced telomere attrition60 that may be independent of its effects on body weight.21 A 2020 MR study using data from the UK Biobank showed that physical activity is inversely associated with breast and colon cancer risk, independent of its effect on adiposity, and the association between physical activity and cancer incidence at ten sites was shown to be independent of BMI.61 Furthermore, strength training, which builds skeletal muscle mass, is inversely associated with the risk of bladder, kidney and colorectal cancer.63 Improvements in insulin sensitivity and glucose homoeostasis induced by aerobic exercise and/or strength training64 could reduce the risk of cancers associated with insulin resistance , including cancers of the colon, liver, pancreas and endometrium.65Whilst reduced caloric intake plays a greater role than physical activity in weight loss,66 Whilst there has been some promising evidence for the beneficial role of fruit and vegetables in reducing cancer risk, the overall impact on cancer burden is largely limited to cancers of the respiratory and upper digestive tract.67 Furthermore, enthusiasm for micronutrient supplementation to reduce cancer risk has diminished following a number of randomised control trials that have produced evidence of an associated increased risk of cancer.69 The lack of the impact of single nutrients/foods on cancer prevention does not mean that the quality of the diet can be ignored. Cancers arising from aberrant metabolic pathways are likely to be influenced by the same nutrients and foods that are associated with the risk of diabetes,70 and there is some evidence that healthy dietary patterns  are beneficial. In turn, foods that promote weight gain , along with red and processed meats and alcohol, should be minimised—alcohol consumption is not only a contributor to caloric intake but also a recognised carcinogen.17Similarly, it is important to consider the independent impact of dietary factors both in terms of macronutrient and micronutrient composition. Strong evidence exists for a protective role of several dietary factors in colorectal cancer  but less so for other cancer sites.71 implementation of equitable interventions that impact on several key areas of lifestyle offer considerable scope for reducing the overall disease burden. Although many unanswered questions exist within lifestyle interventions, with respect to dose, duration, type , caloric composition and diet quality (in terms of food intake), and how best to support long-term adherence, there is much that we can learn from longer-term lifestyle trials including those focusing on diabetes prevention. For example, intervention design no longer focuses on knowledge exchange alone, but integrates goal-based behavioural interventions, the use of lifestyle coaches, frequent contact and support and ‘toolbox strategies' to enable individual tailoring.72 Furthermore, recent work has highlighted the impact of using behavioural change techniques to support changes in diet and physical activity.73Focus on weight management enables a lifestyle pattern combining diet quality and quantity, alcohol intake and physical activity to be promoted and tested. Given the tendency for lifestyle behaviours to cluster/co-occur,74 to address challenges such as selective reporting, small sample size, short length of follow-up and high costs . These challenges are common in nutritional trials , and it is clear that the methodological rigour of complex dietary behavioural trials needs to improve. In reality, large randomised controlled trials are likely to improve our understanding of the impact of weight management on cancer risk, but will need to be considered alongside other data sources such as pooled cohort studies,75 triangulated MR approaches , comparator groups, adaptive trial design79 and to collect long-term outcomes. The key here is to assess the body of evidence appropriately by recognising the inherent weaknesses in the various research designs that contribute to it.The potential for ‘megatrials’ to answer nutritional questions has been described by Trepanowski and Ioannidissee Fig. 76 and ne80) for this lack of investment points to a lack of good interim biomarkers, the need for prohibitively large sample sizes, uncertainties about life stage and appropriate ‘dose’ of intervention, the need to achieve sustained behaviour change and the apparent desire for genetic discoveries. There are also concerns that people who attempt and fail to adhere to weight-loss regimens might experience negative emotional responses and, indeed, self-blame if a subsequent diagnosis of cancer is made. However, the past decade has seen a portfolio of weight-loss regimens combining novel dietary approaches, motivational technologies and implementation science approaches, which will help to optimise adherence and provide supportive behaviour change strategies for weight-loss trials.82 Although multicomponent interventions offer significant challenges, such approaches have been successfully tested in diabetes83 and cognitive function84 contexts, and are feasible to implement. Modern wearable technologies to motivate and support behaviour change, remote objective data collection and record linkage to routine clinical or registry data for follow-up (of at least a decade) make some of the difficulties in cancer-prevention trials more manageable. Furthermore, improvements in trial design, understanding of intervention content and dose and knowledge regarding the provision of effective long-term support for behaviour change make successful cancer-prevention trials increasingly plausible. Nevertheless, an important challenge for primary prevention trial design is the identification of clinically meaningful short- and longer-term health outcomes. The search for robust and clinically relevant surrogate markers  continues, and such markers would add considerable confidence to expensive intervention studies with long-term follow-up. However, it is also important to note that studies of chemoprevention  that have cancer development as their primary outcome have been funded, and lifestyle interventions could do likewise.Although three decades of trials of behavioural weight-loss programmes such as the Diabetes Prevention Programme have successfully demonstrated a significant reduction in the incidence of diabetes, weight -loss programmes for cancer prevention have not received much funding. A 21st- century rationale  more likely to develop colorectal cancer than participants with under- and normal weight, and their risk increased by 7% for each 1 kg/m2 increase in BMI. There is considerable interest in weight management in women with a family history of breast cancer, although the greatest efforts to date have focussed on physical activity interventions. Gramling et al.86 reported from the Women’s Health Initiative observational study that healthy lifestyles  led to a reduction in the risk of breast cancer in postmenopausal women, and the degree of this benefit was similar for women with and without a family history of breast cancer. A review by Pettapiece-Phillips et al.87 reported evidence of a protective role of a healthy body size and regular physical activity among BRCA mutation carriers, notably in adolescence and early adulthood. A number of feasibility or pilot trials of weight management have been undertaken in this high-risk population, including an assessment of the Diabetes Prevention Programme (with modifications) on breast cancer risk biomarkers.88 Intervention studies involving diet and physical activity,89 intermittent energy restriction,90 endurance training and nutrition counselling on the Mediterranean diet81 in individuals at increased risk of breast cancer are currently underway. These developmental studies point to the feasibility of initially ‘testing’ complex intervention trials in high-risk populations and should provide both rational and relevant platforms for planning definitive average-risk population-level randomised controlled trials.One notable population of interest for weight-management trials includes people who are known to be at a higher risk of developing cancer, including those with a family history of colorectal or breast cancer who are already undergoing surveillance procedures. In a large international multicentre trial of aspirin in patients with Lynch syndrome , Movahedi et al.91 Worldwide, excess weight is associated with the development of at least 480,000 new cancer cases each year.7 The bulk of current observational evidence on weight loss and obesity-related cancers suggests that decreasing body weight, reducing EBF and maintaining losses, by even relatively modest amounts, can have an impact on future cancer risk. It is important to note that most obese people who lose weight will remain in the obese category, but will have reduced cancer risk by even modest weight loss per se, which should therefore increase motivation for participating in interventions. However, clear gaps remain in our understanding of how changes in body fat or increased levels of physical activity are mechanistically linked to a decreased incidence of cancer. In addition, understanding the impact of different measures of EBF  adds to the complexity of identifying possible solutions.92 These gaps need to be urgently addressed to expedite the development and implementation of future cancer-control strategies.The need for much greater investment in research into cancer prevention is beyond question, and yet the current expenditure is only around 3% of the UK cancer research budget.93 This need is urgent and the time to act is now!Well-designed trials, providing robust evidence of impact, are crucial for efforts to garner funding for weight-management programmes aimed at reducing cancer risk. To date, trials of weight management and cancer prevention have almost exclusively been confined to feasibility work. The time has come for an international commitment to decreasing cancer burden and this commitment includes the development of large-scale intervention trials of weight management for primary prevention of obesity-related cancer—a point also raised in the paper on critical research gaps and recommendations in colorectal cancer.


Despite the biological link between thyroid hormones and breast cancer cell proliferation shown in experimental studies, little is known about the association between hyperthyroidism and breast cancer, as well as its association with the most common mammographic and genetic risk predictors for breast cancer.n = 3,793,492, 2002–2011) and the Karolinska Mammography Project for Risk Prediction of Breast Cancer . We used logistic regression to estimate the odds ratios (ORs) of hyperthyroidism according to the mammographic and genetic risk predictors for breast cancer.This study estimates the incidence rate ratios (IRRs) of breast cancer among women diagnosed with hyperthyroidism, compared to those who are not, using two cohorts: a Swedish national cohort of the general female population , hyperthyroidism was associated with a high polygenic risk score (PRS) for breast cancer overall  and for estrogen receptor-positive specific PRS .An increased risk of breast cancer was observed in patients in the national cohort with hyperthyroidism , particularly for toxic nodular goiter . Hyperthyroidism was associated with higher body mass index, early age at first birth, and lower breastfeeding duration. Higher mammographic density was observed in women with toxic nodular goiter, compared to women without hyperthyroidism. Additionally, among genotyped women without breast cancer in the KARMA cohort (Hyperthyroidism is associated with an increased risk of breast cancer, particularly for patients with toxic nodular goiter. The association could be explained by higher mammographic density among these women, as well as pleiotropic genetic variants determining shared hormonal/endocrine factors leading to the pathology of both diseases. Breast cancer is the most common cancer diagnosed among women worldwide and the leading cause of cancer deaths among women . Breast Several population-based studies have reported hyperthyroidism (excessive production of thyroid hormones) to be associated with breast cancer –12, whilAlthough it is shown that thyroid hormones are associated with breast tissue proliferation and a subsequent increase in breast cancer risk , 4, the Besides the direct effect of thyroid hormones on breast cancer risk, the association between hyperthyroidism and breast cancer could also be explained by genetic pleiotropic pathways leading to both hyperthyroidism and breast cancer . A recenIn this study, we investigated mechanisms behind the association between hyperthyroidism and breast cancer. Specifically, whether a genetic susceptibility to breast cancer, measured using polygenic risk score and mammographic density, could explain some of this association, while being able to account for important factors such as menopausal status, body mass index (BMI), and reproductive health.To study the association between hyperthyroidism and breast cancer risk, we used two cohorts: 1) a Swedish national cohort of the general female population and (2) a mammographic screening-based cohort, Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) . Having a main diagnosis of hyperthyroidism was retrieved from the Swedish Patient Register and classified according to the International Classification of Diseases (ICD) code E05. Types of hyperthyroidism were defined by the ICD codes E050 (Graves’ disease), E051–E052 (toxic nodular goiter), and E053–E059 (other or unspecified types). Follow-up of the cohort ended on the date of breast cancer diagnosis, date of death, date of emigration, or end of follow-up , whichever came first. Information on breast cancer diagnosis, death, and emigration was obtained by using unique personal identification numbers to link the cohort to the Swedish Cancer Register, Swedish Cause of Death Register, and the Swedish Migration Register. The Swedish Cancer Register was founded in 1958. The completeness and accuracy of this register are estimated at 99 and 96%, respectively [The national cohort included all women above age 20 years, who lived in Sweden between 2002 and 2011  of breast cancer-associated risk allele for SNP k, xk is the number of alleles for the same SNP , and n is the total number of the breast cancer SNPs included in the profile. The SNPs and corresponding betas used to derive the PRS are summarized in Additional file Blood samples from a subset of 11,991 women who did not have breast cancer when they joined the KARMA cohort were genotyped. These women were part of the Breast Cancer Association Consortium and were randomly selected as controls for the genome-wide association studies (GWAS) , 24. GenFull-field digital mammograms from mediolateral oblique views of the left and right breasts in the most recent screening before 2017 were used. For the KARMA cohort, we used the area-based STRATUS method to measure mammographic density . PercentThe study was approved by the Regional Ethical Review Board in Stockholm, Sweden .For both cohorts, the incidence rate ratio (IRR) of breast cancer among hyperthyroidism patients was calculated using Poisson regression, using attained age as the time scale and adjusting for calendar period (3-year categories). For these analyses, hyperthyroidism was treated as a time-varying exposure, in which the exposed person-time was counted from 3 months after the hyperthyroidism diagnosis (index date) and the unexposed person-time was counted from Jan 1, 2002, and ended on the date of breast cancer diagnosis, date of death, date of emigration, index date, or end of follow-up, whichever came first. The analysis was further stratified by menopausal status, age and time since hyperthyroidism diagnosis, and type of hyperthyroidism. In the analyses, Model 1 adjusted for calendar period and Model 2 further adjusted for BMI, age at menarche, number of births, family history of breast cancer, oral contraceptive use, hormone replacement treatment, and having had a benign breast disease.The association between hyperthyroidism and various lifestyle risk factors for breast cancer were assessed among KARMA women using logistic regression models. These risk factors included having had a benign breast disease, number of births, age at first birth, breastfeeding duration, BMI, family history of breast cancer, oral contraceptive use, hormone replacement therapy use, age at menarche, and menopausal status. Two models were conducted for this analysis, a univariable model and a multivariable model adjusting for all these risk factors simultaneously.Given that mammographic density is causally related to breast cancer risk, we also tested the association between having a prior diagnosis of hyperthyroidism and mammographic density among KARMA women using logistic regression models. Model 1 was the univariable model, Model 2 adjusted for age at mammogram and BMI, and Model 3 further adjusted for age at menarche, number of births, family history of breast cancer, oral contraceptive use, hormone replacement treatment, and having had a benign breast disease. Linear regression models were also used to test the effect of hyperthyroidism on mammographic density (square root transformed) as a continuous variable.To test the association between hyperthyroidism and having a genetic predisposition to breast cancer, we used logistic regression models to calculate the ORs of hyperthyroidism among KARMA women who had been genotyped, by quartiles of PRS for breast cancer overall, for ER-positive cancers, and for ER-negative cancers. These associations were adjusted for age, the first three principle components, and for genotyping array. We further adjusted for age at menarche, number of births, family history of breast cancer, BMI, menopausal status, oral contraceptive use, hormone replacement treatment, and having had a benign breast disease.Statistical analyses were performed using SAS  and Stata software .In the national cohort, 389 cases of breast cancer patients were observed during 133,154 person years following a diagnosis of hyperthyroidism, corresponding to an incidence rate of 2.9/1000 person years. Patients with hyperthyroidism experienced a 23% increased risk of breast cancer . In the KARMA cohort, there were 36 cases of breast cancer after hyperthyroidism diagnosis during a follow-up time of 7930 person years, corresponding to an incidence rate of 4.5/1000 person years. KARMA women with a diagnosis of hyperthyroidism also had a 22% increased risk of breast cancer, although the association was not statistically significant in the multivariable adjusted model . In both cohorts, women diagnosed with hyperthyroidism aged younger than 40 years had a significantly increased risk of breast cancer, while there was no strong difference for the risk of breast cancer according to menopausal status , which was predominantly driven by toxic nodular goiters , even after adjusting for all other potential risk factors for breast cancer . This association was mainly driven by the association between hyperthyroidism and ER-positive breast cancer  . The association between hyperthyroidism and breast cancer was further supported by an increased risk of breast cancer among women with high serum thyroxine (T4) [Our estimates for an increased risk of breast cancer among women diagnosed with hyperthyroidism were the same when analyzing data from the national and KARMA cohorts, which is also consistent with previous estimates in Denmark and Taiwan , 12. Thiine (T4) , 28. Desine (T4) , 30, whiine (T4) .Several biological pathways may exist between hyperthyroidism and breast carcinogenesis. T3 could activate the PI3K pathways mediated by integrin αvβ3 and stimulate breast cancer cell invasion . In addiIn addition to the known association between thyroid hormones and breast cancer risk, it has been hypothesized that I-131 (radioactive iodine) treatment for hyperthyroidism may also increase the risk of breast cancer. However, majority of the studies did not find a significant association between I-131 treatment and breast cancer –40. AfteIn this study, hyperthyroidism was associated with several risk factors for breast cancer. Both Graves’ disease and toxic nodular goiter are strongly influenced by pregnancy , 45, whiWe found a significantly higher BMI among patients with hyperthyroidism, despite previous knowledge that reduced weight is associated with hyperthyroidism . This diIndependent of BMI, toxic nodular goiters were associated with higher mammographic density. While one previous study did not find a significant association between thyroid disorders (including hyperthyroidism and thyroid nodules) and mammographic density, this is likely due to having a limited sample size . This fiAdditionally, we found a significant association between breast cancer PRS and hyperthyroidism. Among those GWAS significant SNPs for breast cancer, previous studies show that one SNP in the ABO gene is associated with thyroid function , 56. AnoConsidering the phenotypic and genetic associations between hyperthyroidism and breast cancer, as well as the beneficial effect of introduced euthyroid hypothyroxinemia in the setting of breast cancer , hyperthThe main strength of our study is having a large sample size and a population-based cohort design, including both Swedish health registers and a mammographic screening cohort. Other strengths include the KARMA cohort containing rich lifestyle, mammographic and genetic data, which allowed us to explore underlying mechanisms for the associations investigated. We also acknowledge several limitations. The validity of ICD codes in the Swedish patient register is about 85–95% , which iIn conclusion, we found that women diagnosed with hyperthyroidism were associated with an increased risk of breast cancer and major predictors for breast cancer, including mammographic density and polygenic risk score. These findings may partly be explained by shared genetic and hormonal factors between these two diseases and may contribute to a more comprehensive understanding of hormonal/endocrinal factors contributing to breast cancer risk.Additional file 1: Table S1. Characteristics of women in the national and KARMA cohorts, by hyperthyroidism status. Table S2. Single nucleotide polymorphisms (SNPs) used for constructing the polygenic risk score (PRS) for breast cancer. Table S3. The association between hyperthyroidism and major breast cancer risk predictors among the KARMA cohort, by type of hyperthyroidism . Table S4. The association between hyperthyroidism and mammographic density among KARMA women without cancer, by type of hyperthyroidism . Figure S1. Sample attrition for the analyses using the KARMA cohort


A high body mass index (BMI) has been associated with increased risk of several cancers; however, whether BMI is related to a larger number of cancers than currently recognized is unclear. Moreover, whether waist circumference (WC) is more strongly associated with specific cancers than BMI is not well established. We aimed to investigate the associations between BMI and 26 cancers accounting for non-linearity and residual confounding by smoking status as well as to compare cancer risk estimates between BMI and WC.Prospective cohort study with population-based electronic health records from Catalonia, Spain. We included 3,658,417 adults aged ≥ 18 years and free of cancer at baseline between 2006 and 2017. Our main outcome measures were cause-specific hazard ratios (HRs) with 99% confidence intervals (CIs) for incident cancer at 26 anatomical sites.2 increment) ranged from 1.04 (99%CI 1.01 to 1.08) for non-Hodgkin lymphoma to 1.49 (1.45 to 1.53) for corpus uteri cancer. While BMI was negatively associated to five cancer types in the linear analyses of the overall population, accounting for non-linearity revealed that BMI was associated to prostate cancer in a U-shaped manner and to head and neck, esophagus, larynx, and trachea, bronchus and lung cancers in an L-shaped fashion, suggesting that low BMIs are an approximation of heavy smoking. Of the 291,305 participants with a WC measurement, 27,837 were diagnosed with cancer. The 99%CIs of the BMI and WC point estimates (per 1 standard deviation increment) overlapped for all cancers.After a median follow-up time of 8.3 years, 202,837 participants were diagnosed with cancer. A higher BMI was positively associated with risk of nine cancers  and was positively associated with three additional cancers among never smokers . The respective HRs  cancers. Furthermore, BMI and WC showed comparable estimates of cancer risk associated with adiposity.Supplementary information accompanies this paper at 10.1186/s12916-020-01877-3. The prevalence of obesity worldwide has nearly tripled over the past three decades, reaching 650 million adults in 2016 , 4. HoweIn addition, whether BMI as a sole indicator of general adiposity fully captures the complex association between adiposity and cancer risk is still in dispute. Central adiposity, typically assessed using waist circumference (WC), has been suggested to increase the risk of several cancer types and to better discriminate risk associated with obesity for colon and breast post-menopausal cancers –8. HowevThe primary objective of the current study was to investigate associations between BMI and the risk of 26 types of cancer accounting for non-linearity and residual confounding by smoking status. Our secondary objective was to compare risk estimates for general (BMI) and central (WC) adiposity in relation to the risk of 26 cancer types.www.sidiap.org), from January 1, 2006, until December 31, 2018. SIDIAP includes routinely recorded information by health professionals from 287 primary care centers in Catalonia, a region in Northeastern Spain [We performed a cohort study with prospectively collected data from the Information System for Research in Primary Care  recorded between January 1, 2006, and December 31, 2017, and subsequent eligible follow-up time (minimum of 1 year). The study’s index date was the date of the first BMI assessment during this period. We followed participants from the study index date until first incident (primary) cancer diagnosis, death, transferal out of the SIDIAP, or until the end of the study period . We excluded individuals who were older than 100 years of age at index date, had a BMI assessment only available during pregnancy (from the 3rd month of pregnancy until 2 months after delivery), had any record of cancer before the study index date, or complied with any of the end-of-follow-up criteria described above before attaining 12 months of follow-up to avoid reverse causality /height (m)2). BMI was automatically calculated through a computer program (“Estació clínica d’atenció primària”) after general practitioners (GPs) or nurses entered the weight (kg) and height (cm) of patients they directly assessed in a standardized manner [For our primary objective, the exposure of interest was BMI as a continuous variable , age (in years), and geographic region of nationality . We assessed socioeconomic status in urban areas using the “Mortalidad en áreas pequeñas Españolas y Desigualdades Socioeconómicas y Ambientales” (MEDEA) deprivation index, which is calculated at the census tract level and was categorized into quintiles by the SIDIAP for anonymization purposes . The firWe considered first incident cancer diagnoses as the outcomes of interest. We identified outcomes using ICD-10 codes in the SIDIAP database and ICD-9 codes in the CMBD from January 1, 2007, to December 31, 2018. We mapped ICD-9 diagnosis codes to ICD-10 using available conversion codes (eCIEMaps v3.1.9) which are provided in Additional file 2 and between ≥ 18.5 and < 25 kg/m2), overweight (BMI ≥ 25 and < 30 kg/m2), and obesity (BMI ≥ 30 kg/m2).We described the number of excluded individuals in each step of the creation of the main dataset. We presented the overall baseline characteristics of the study participants and by the World Health Organization (WHO) BMI categories: underweight or normal weight  and 99% confidence intervals (CI) for the relation between BMI and risk of each cancer type. We stratified all models by age (5-year categories) and sex to reduce the sensitivity to violations of the proportional hazards assumption. The first (basic) model included BMI only model 1) and the second (multivariable-adjusted) model further adjusted for smoking status, alcohol intake, type 2 diabetes, socioeconomic status, and nationality (model 2). A directed acyclic graph was used to guide decisions on the control for confounding  or 5 knots . We evaluated linearity by comparing the Akaike information criterion of models with restricted splines to the model with BMI as a linear term in combination with a Wald test linearity hypothesis , 21. To 2 increment) and risk of each cancer type. Again, we re-run the multivariable-adjusted models  only among participants who reported having never smoked to explore residual confounding by smoking.Secondly, we fitted model 2 with BMI as a linear term to estimate HRs of the relation between BMI  by extending the minimum follow-up time  to 2 and 4 years. Fourth, we strengthened the validity of our results by performing multiple imputations  to deal with missing values of model 2 covariates , 24. FifTo assess the robustness of our secondary findings, we performed two sensitivity analyses. We re-ran the analyses that compared BMI and WC in relation to cancer risk with mutual adjustment for both adiposity indicators using residuals of WC and BMI  to assess if this added valuable information to fully capture adiposity . FinallyP value of 0.01 for all analyses. We used STATA version 15.1  for data analysis and R version 3.5.0 for data visualization.The a priori level of statistical significance was set at a 2-sided We obtained approval from the Clinical Research Ethics Committee of the IDIAPJGol (project code: P14/074) to perform this study. 2 (IQR 23–30). When stratifying participants by WHO categories of BMI, the median follow-up and age increased with increasing categories of BMI. There were fewer participants from deprived areas and more current smokers in the underweight and normal weight category compared to those in the obesity category  . These non-linear relations disappeared when we restricted the analyses to never smokers  Fig. . For caners Fig. .Fig. 2A2, at which point the increase in risk diminished. For prostate cancer, the risk curve displayed an attenuated inverse U-shape, with a lower risk of cancer among those with low, normal, and very high BMIs, but an increased risk for those in the overweight range. For corpus uteri cancer, the risk increased faster than linear at higher BMI values. Finally, the association between BMI and Hodgkin lymphoma was J-shaped, with a modest higher risk of this lymphoma in people with low BMIs and a more markedly higher risk for those with high BMIs.The curves for the associations between BMI and risk of cancers of the liver, breast post-menopausal, corpus uteri, prostate, and Hodgkin lymphoma were non-linear and were similarly shaped in the overall cohort and among never smokers Figs.  and 3. L2  was positively associated with risk of cancers of the corpus uteri , kidney , gallbladder and biliary tract , multiple myeloma , thyroid , leukemia , colorectal , breast post-menopausal , and non-Hodgkin lymphoma  , Hodgkin lymphoma (63), testis 52), and breast pre-menopausal (44)   were similar to those from the sensitivity analyses. The CIs of the sensitivity analyses consistently included the main point estimate with only two exceptions . After restricting the analyses to never smokers to account for incomplete adjustment for smoking, BMI was also positively associated with Hodgkin lymphoma and cancers of the headandneck, and brainand CNS. BMI was associated in an inverse U-shaped manner with the risk of prostate cancer and in an L-shaped fashion with the risk of four cancers  in the overall cohort likely indicating residual confounding by smoking since the shape of these associations drastically changed among never smokers, except for prostate cancer.In this prospective study that included 3,658,417 participants and 202,837 cancer cases, we found that a higher BMI was associated with risk of 18 of 26 cancer types, although these relations differed in terms of direction, shape, and smoking status at baseline. BMI was positively associated with risk of cancers of the In a subsample of 291,305 participants with a WC measurement and 27,837 cancer cases, we compared cancer risk estimates of WC and BMI. The 99% CIs of the WC and BMI effect estimates consistently overlapped, indicating that WC provides risk associations similar to BMI across a wide range of cancer types in our population.This study has several strengths. Firstly, to our knowledge, this is the first study to systematically compare both BMI and WC indicators in relation to the risk of a wide variety of cancers, including less frequently occurring ones. Secondly, owing to the large scale of the SIDIAP database, we were able to investigate the association between BMI and numerous cancer types in a Southern European region, increasing the external validity of results previously reported in Northwestern European countries , 4. Lastprostate cancer). Fifth, while the magnitude of this study’s sample size has its advantages, some of the significant findings of this study could have been related to the large sample size. Another limitation was the missing covariate data which ranged from 10% (for the MEDEA deprivation index) to 39% . However, the results from our main analysis did not differ when we performed multiple imputations of these data  could result in selection bias. However, the study participants were not substantially different from the overall SIDIAP population  were L-shaped, suggesting that low BMIs are an approximation of heavy smoking. In the linear analyses restricted to never smokers, the associations between BMI and cancers of the larynx and esophagus became null, likely due to the opposite effects of BMI in adenoma and squamous cell carcinoma . Also, a2. In addition, some studies described a positive association between BMI and cancers of the liver and stomach [There were also differences between our results and those of previous studies. Despite the evidence supporting the inverse association between BMI and risk of breast pre-menopausal cancer , we obse stomach , 43. OurIn a post hoc analysis, modeling height and weight in mutually adjusted models, we found that the nine and five cancer types that were positively and negatively, respectively, associated with BMI (in linear models) were also all associated with weight in the same directions. On the other hand, height was positively associated with 14 cancer types (and only negatively associated with corpus uteri cancer)  cancers, and we confirmed associations reported in previous studies. Moreover, this study showed that BMI and WC result in comparable estimates of cancer risk associated with adiposity at a population level.While the observational nature of this study prevents us from making policy and clinical recommendations, our findings reinforce the need for public health strategies focusing on the reduction of obesity for cancer prevention and indicate that assessing obesity-related cancer risk in primary care using BMI may be sufficient.Additional file 1: Appendix 1. definition of menopause and use of hormonal replacement therapy variables. Appendix 2. STROBE Statement-Checklist. Table S1. diagnostic codes used to define cancer cases. Table S2. characteristics of individuals with and without a BMI recorded. Table S3. BMI-cancer risk associations: results of the basic adjustment models. Table S4. P for non-linearity in WC-cancer risk associations. Table S5. A wide range of sensitivity analyses of BMI-cancer risk associations. Table S6. Sensitivity analyses of BMI-cancer risk associations using cancer registry data to confirm SIDIAP cases. Table S7. Sensitivity analyses of BMI-cancer risk associations excluding subgroups of participants. Table S8. Sensitivity analyses of BMI-cancer risk associations for women only cancers. Table S9. Sensitivity analysis including results of BMI/height/weight-cancer risk associations. Table S10. Sensitivity analysis of BMI/WC-cancer risk associations including additional adjustment for height. Table S11. Comparison of BMI information recorded in the SIDIAP and other studies’ data. Table S12. BMI-cancer risk associations stratified by sex. Figure 1. Directed Acyclic Graph that guided our decisions in the control for confounding. Figure 2. Sensitivity analysis of BMI/WC-cancer risk associations, including mutual adjustment using residuals of BMI and WC.


M = 43.26; SD = 12.43), 46.7% were breast cancer survivors, and 53.3% were healthy controls. The selection of women with breast cancer carries out randomly, recruited through the Oncology Units. The sample completed measures of resilience and PEI, through Resilience Scale   can act as a risk or protective factor in the development of resilience. The total sample was 167 women (Age:  Cancer is one of the most significant diseases due to its high incidence and morbidity, being one of the leading causes of death worldwide  assuming an important influence on physical and psychological well-being  can mediate the relationship between breast cancer and resilience. It is hypothesized that breast cancer survivorship is associated with greater resilience and that these levels of resilience will be even better if associated with better emotional clarity and/or repair. At the same time, another pathway could occur whereby high levels of emotional attention would be associated with lower levels of resilience.M = 43.26; SD = 12.43). The total sample indicated that 52.7% were married, 31.7% were single, 7.2% were common-law partners, 5.4% were divorced, and 3% were widows. Regarding the work status, 34.1% were employed workers, 23.4% were unemployed, 15.6% were pensioners, 14.4% were housewives, 7.8% were on sick leave, and 4.8% were self-employed.Study participants were 167 Spanish women with an age range between 18 and 69 years (The participants were divided into two groups. Group “Breast cancer survivors” was composed of 78 breast cancer survivors (46.7%) and Control Group were 89 women without any oncological disease (53.3%). The common inclusion criteria for both groups were: (1) To be a woman; (2) To have more than 18 years; (3) To have an adequate cognitive ability to understand the psychological tests presented; (4) To not receive at the time of assessment, psychiatric and/or psychological treatment; and (5) To not be under the influence of psychoactive medication. Besides, for the breast cancer survivors group was taken into consideration: (1) To have been diagnosed with breast cancer at least 1 year before to participate in the study or to have received the medical discharge.Breast cancer survivors were randomly recruited from the Oncology Units of the reference hospitals of the province of Cadiz, Spain. They were recruited with the help of doctors from the oncology units, women who met the participation criteria were informed about the study, and all the people who showed their interest were put in contact with the research team. Thus, finally, they were contacted, and they met individually.Something similar was done with Control Group. In this case, the recruitment was carried out through social networks and the general population. Women in Group 2 were selected with similar socioeconomic and educational characteristics than Group 1 patients.To carry out this study approval was obtained from the Ethics Committee of the referral hospitals (project identification code: PIN0109-2018). Prior participation, they had to sign an informed consent. Data collection was performed through paper-and-pencil questionnaires and were analyzed according to guidelines of the Declaration of Helsinki of the World Medical Association (WMA) .Wagnild and Young Resilience Scale , medium effect (between 0.21 and 0.5) or large (>0.50). These analyses were achieved with the statistical software SPSS 22 .Preliminary analyses were carried out to compute descriptive statistics and internal consistencies for measures through Cronbach's alpha. Hence, we compared mean scores of both groups (breast cancer survivors and control group) by performing an analysis of variance, including Cohen's Finally, a serial multiple mediation analysis was performed to test whether emotional attention, emotional clarity and mood repair mediated the relationship between breast cancer survivorship and resilience after controlling for the influence of age. So in this study, breast cancer survivors cancer was considered the first variable, as a binary variable (0 = control group and 1 = breast cancer survivors group)  and resilience as the outcome (Y). Emotional Attention (M1), Emotional Clarity (M2), and Emotional Repair (M3) were considered the mediator variables, and (e) was age as a covariate. This total effect was the sum of all possible pathways from X to Y, direct (c′), or mediating pathways (the indirect effects). The direct effect (c′) refers to the relationship between survival to breast cancer and resilience, after controlling for the mediators. The indirect effects are: the total indirect effect (a) represents the association between the predictor and the three mediators ; the total indirect effect (b) refers to the role of the three mediators about resilience ; the total indirect effect (d) refers to the relationship of the three mediators to each other , and the specific indirect effect  refers to the role of a specific mediator in the relationship between breast cancer survival and resilience. Furthermore, finally, age covariate (e) is established with each mediator (M) and result variable (Y). All mediation analyses described below were estimated with the PROCESS macro Hayes,  using SPF = 68.24, p < 0.001, d = −13.43], resilience , and mood repair  showing that breast cancer women exhibited greater levels of these variables, with a high effect size. Nevertheless, there were no statistically significant differences in emotional attention , and emotional clarity .Concerning comparison in variables by groups, the analysis of variance (ANOVA) indicated statistically significant differences in age [As shown in p > 0.05). Correlations between emotional clarity (r = 0.331) and mood repair (r = 0.382) with resilience are observed for the control group and the dimensions of emotional intelligence are correlated in such a way: attention with clarity (r = 0.307) and clarity with repair (r = 0.648). The same relationships are shown in the cancer group; emotional clarity (r = 0.367) and mood repair (r = 0.566) with resilience and attention with clarity (r = 0.261) and clarity with repair (r = 0.470).The statistically significant correlations between all the study variables in each group are described below (R2 = 0.05) of the variance of resilience being non-statistically significant. However, when the PEI dimensions were included in the model, the explanatory ability of the entire model was increased to 28% (R2 = 0.28) in a statistically significant way.As shown in 3: β = 3.03, p = 0.00), emotional clarity and mood repair on resilience , emotional attention on emotional clarity , and the latter with mood repair . Besides, other statistically significant direct effects indicated a negative association between age and emotional attention , and emotional attention with a decrease in mood repair , and resilience .Regarding the direct effects, we found positive predictive associations of breast cancer survivorship on mood repair  showed statistical significance in the model. Thus, overcoming breast cancer was associated with a greater ability to repair adverse emotional states, which in turn increased women's resilience . In this case, a full mediation effect was found because the variation in resilience is explained only through the mediator, specifically mood repair, and not by the direct effect of breast cancer survivorship. The diagram of the model is summarized in In respect of indirect effects, only indirect effect 3 , so that the results could be explained partly due to the variance bias of the common method. Besides, the causal interpretation is problematic because it is a cross-sectional study.Future research should establish moderation models to know which levels of this variable are adaptive or not. Even a more detailed analysis of TMMS-24 in its attentional dimension would be interesting, for example, analyzing which items are more associated with adverse or maladaptive results and which elements would be more associated with adaptive results. Furthermore, it would be of interest to test this model in other types of cancer, even in patients with treatment, it will facilitate the generalization of our results throughout the oncological population. The medical characteristics of cancer should also be taken into account in other studies because it will allow to reveal possible variations in the mediation model and will understand how they influence the reaction to the disease, promoting or inhibiting a resistant response. Also, incorporating other variables, such as the social and interpersonal dimensions of emotional intelligence, into the compression of this mediation, would be interesting.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by e-Health for Quality of Life and Health promotion in Breast Oncology PIN0109-2018. ETHICS COMMITTEE MINISTRY OF HEALTH (JJVM/apg). The patients/participants provided their written informed consent to participate in this study.RG developed the project design and data analysis, data preparation, coding and writing the manuscript, and approved the final version of the manuscript for submission. PR-G contributed to project design, survey creation, performed the collection, the data analysis, interpretation, and writing of manuscript. AM-C contributed to the interpretation, writing of the manuscript, and approved the final version of the manuscript for submission. LM-S, AZ, and RG-M contributed to project design and data analysis, data preparation, coding and interpretation, and writing the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Although the 21-gene recurrence score (RS) assay is widely used to predict distant recurrence risk and benefit from adjuvant chemotherapy among women with hormone receptor-positive (HR+) breast cancer, the relationship between the RS and isolated locoregional recurrence (iLRR) remains poorly understood. Therefore, we examined the association between the RS and risk of iLRR for women with stage I-II, HR+ breast cancer.We identified 1758 women captured in the national prospective Breast Cancer-Collaborative Outcomes Research Database who were diagnosed with stage I-II, HR+ breast cancer from 2006 to 2012, treated with mastectomy or breast-conserving surgery, and received RS testing. Women who received neoadjuvant therapy were excluded. The association between the RS and risk of iLRR was examined using competing risks regression.n = 329) had a RS ≥25. At median follow-up of 29 months, only 22 iLRR events were observed. Having a RS ≥25 was not associated with a significantly higher risk of iLRR compared to a RS < 25 . When limited to women who received adjuvant endocrine therapy without chemotherapy , having a RS ≥25 (n = 74) was significantly associated with a higher risk of iLRR compared to a RS < 25 . In this group, increasing RS was associated with greater risk of iLRR .Overall, 19% of the cohort (The RS was significantly associated with risk of iLRR in patients who did not receive adjuvant chemotherapy. The utility of the RS in identifying patients who have a low risk of iLRR should be further studied. Gene expression profiling has emerged as a useful clinical tool for patients with early-stage breast cancer. One such gene expression assay, the 21-gene recurrence score (RS), has been demonstrated to add utility to traditional clinicopathologic factors for prognosticating distant recurrence risk and predicting response to adjuvant chemotherapy in patients with early-stage, hormone receptor-positive (HR+) breast cancer . Yet, thImproving our ability to prognosticate the risk of iLRR among women with early-stage breast cancer could allow for a more individualized approach to use of postoperative radiation therapy (RT), an area of active investigation. The CALGB 9434 trial found that RT decreased the 10-year locoregional recurrence from 10 to 2% for women age ≥ 70 years with early-stage breast cancer treated with breast-conserving surgery (BCS) and tamoxifen . NeitherThe Breast Cancer-Collaborative Outcomes Research Database (BC-CORD) is a national, prospective database of patients with newly diagnosed breast cancer who received care at one of eight participating cancer centers in the United States. Using BC-CORD, we identified 1758 women who were diagnosed with stage I-II, HR+ breast cancer from 2006 to 2012, treated with mastectomy or BCS, and whose RS values were known  was estimated considering distant metastases and death as competing risks. The association between the RS and iLRR was examined using univariable competing risks regression [P < 0.05. Statistical analyses were performed using Stata 13.1 . Institutional review board approval was obtained before undertaking this study.Our primary analysis categorized RS as ≥25 vs. < 25. Secondary analyses categorized RS in four ordinal groups . Patient characteristics were compared using Pearson’s χP = 0.81).Study cohort characteristics appear in Table n = 1199) of patients treated with endocrine therapy without chemotherapy, 6% (n = 74) had RS ≥25. In this group, with median follow-up of 30 months (interquartile range 16–72 months), RS ≥25 was associated with a significantly higher risk of iLRR  are listed in Table  6% n = 7 had RS ≥n = 6), chest wall (n = 4), ipsilateral lymph node (n = 6), other locoregional lymph node (n = 1), or concurrently in the breast/chest wall and locoregional lymph node outside the axilla (n = 1). For the 4 women with RS ≥25 and iLRR, these iLRR occurred in the ipsilateral breast (n = 1), chest wall (n = 2), or ipsilateral lymph node (n = 1).For the 18 women with RS < 25 and iLRR, these iLRR occurred in the ipsilateral breast  database and the National Cancer Data Base (NCDB) . Dong etThe RS was significantly associated with risk of iLRR in patients with early-stage, hormone receptor-positive breast cancer who received adjuvant endocrine therapy but not chemotherapy. The utility of the RS in identifying patients who have a low risk of iLRR should be further studied.


P53 gene on the risk of development ofbreast cancer remains variable in populations. However, the use ofstrategies such aspoolingage-matched controls with disease may provide a consistent meta-analysis. Our goal was to perform a meta-analysis in order to assess the association of p.Arg72Pro variant of P53 gene with the risk of breast cancer.The effect of the p.Arg72Pro variant of the TP53 p.Arg72Pro polymorphism were selected. The fixed and random effects (Mantel-Haenszel) were calculated using pooled odds ratio of 95% CI to determine the risk of disease. Inconsistency was calculated to determine heterogeneity among the studies. The publication bias was estimated using the funnel plot.Databases such as PubMed, Genetics Medical Literature, Harvard University Library, Web of Science and Genesis Library were used to search articles. Case-control studies with age-matched on breast cancer havingevaluated the genotype frequencies of the TP53 p.Arg72Pro was associated with the risk of breast cancer for the dominant model  and the additive model , but not for the recessive model . According to the ethnic group analysis, Pro allele was associated with the risk of breast cancer in Caucasians for the dominant model and additive model (P = 0.02), and Africans for the recessive model and additive model (P = 0.03).Twenty-one publications with 7841 cases and 8876 controls were evaluated in this meta-analysis. Overall, our results suggested that TP53 p.Arg72Pro polymorphism and the risk of breast cancer. Individuals carrying at least one Pro allele were more likely to have breast cancer than individuals harboring the Arg allele.This meta-analysis found a significant association between  BRCA1, BRCA2 and P53 which produce tumor suppressor proteins and participate in damaged DNA repair [P53 plays a key role in the regulation of cell proliferation and apoptosis. The P53 protein is essential for maintaining the integrity of the cell and its components. In human cancers, mutated P53 produces abnormal proteins that alter or inhibit transcriptional regulation [P53 gene would lead in linkage disequilibrium in the DNA sequence, which, associated with chromosomal aberrations induce the appearance of genomic instability and later the development of cancer [P53 has been mapped on chromosome 17p13 and contains 11 exons. Several single nucleotide polymorphisms (SNP) have been identified and the most studied variant is the substitution of Arginine by Proline at position 72 in exon 4. Studies carried out on different populations around the world have shown that this SNP is associated with the development of numerous diseases including cancers [P53 gene and the risk of breast cancer, however, the reports from these studies remain conflicting as some studies have shown that p.Arg72Pro is associated with the risk of breast cancer, while others found no association. Menzel et al. 2004 [P53 gene. This heterogeneity may be related not only to the geographic and ethnic origin [P53 gene may represent a potentially important genetic marker, contributing to breast cancer susceptibility in Caucasian, Asians and Africans. The present meta-analysis included only age-matched case-control studies in order to statistically decrease the heterogeneity between the studies, to qualitatively assess the effect of p.Arg72Pro on the risk of breast cancer. We have performed an independent two-stage meta-analysis; overall and sub-group analysis.Breast cancer is a multifactorial disease which constitutes a major public health problem . In 2018gulation . As a ref cancer , 8. The f cancer . P53 has cancers , 11. It al. 2004  and Akkial. 2004 . These dc origin –17 but aP53”, “p.Arg72Pro” and “polymorphism” or “mutation” or “gene” and “breast cancer”cited in the genetic association studies were used to detect and select scientific manuscripts in these databases. We also reviewed references cited in these studies to identify additional articles that were not identified by our research in the databases.The Pubmed Genetics Medical Literature Database, the Harvard University Library, and the Web of Science and Genesis Library were used to identify available articles published in English. The keywords “P53 gene and the risk of breast cancer, (2) full manuscript available, (3) case-control study with age-matched, (4) distribution of genotype respecting Hardy-Weinberg equilibrium (HWE) in controls, (5) availability of the three genotypic frequencies  in the case and control groups. (6) Study no influencing the pooled odd ratio (OR) values. Three investigators independently evaluated each study to determine eligibility.The inclusion criteria included: (1) published case-control studies as an original article to evaluate the association between p.Arg72Pro of the The data were collected by an investigator and verified by a second investigator to reach consensus on all points. First author, year of publication, country, ethnicity of study population, sample size, age-matched, distribution of genotype andalleles, as well as the recalculation of HWE in controls were extracted from the eligible studies. A third reviewer made a contradictory assessment to reconcile the assumptions. The data of controls evaluated with p.Arg72Pro variant were included in this meta-analysis.2 analysis with a significance level of P < 0.05 was used to evaluate whether p.Arg72Pro polymorphism distribution of the P53 gene in controls fits HWE. The association between the p.Arg72Pro and the risk of breast cancer was evaluated by the Odd ratio (OR) of 95% CI. We evaluated the strength of association between the p.Arg72Pro polymorphism of P53 gene and the risk of breast cancer using different genetic models, including the dominant (Pro/Pro + Arg/Pro vs. Arg/Arg), recessive (Pro/Pro vs. Arg/Arg + Arg/Pro) and the additive (Pro vs. Arg). Heterogeneity among the studies was assessed by I2 statistical test [2 > 50% (presence of heterogeneity), the random effects model was used to calculate the overall OR, otherwise in case of lack of heterogeneity, the fixed effects method was used. We also have examined the funnel plot to determine publication bias [Chical test , 19. If ion bias . All staFigure TP53 p.Arg72Pro polymorphism and the risk of breast cancer. Overall, a slightly association of TP53 p.Arg72Pro polymorphism with the risk of breast cancer was observed for the dominant , we noted a moderate association of p. Arg72Pro with the risk of breast cancer for the dominant  and additive  models in Caucasians   than in the additive model .Table p-values for the different genetic models  in the recessive and dominant models. We noted the same tendency of heterogeneity when considering all the data  and among Asians  for the additive model  and Africans (recessive and additive models). These trends were consistent with previous studies [TP53 p.Arg/Pro was associated with the risk of breast cancer in the South Asian population. The difference between the studies could be explained by the ethnicity and study design. The effects of ethnicity may be due to several factors, allelic heterogeneity, gene-gene and gene-environment interaction and linkage disequilibrium [TP53 gene and the risk of breast cancer using different genetic models. However, several limitations need to be highlighted, sample size and small number of case-control age-matched studies in ethnic groups.In the present meta-analysis, we examined the relationship between pulation . These apulation , 53. Thepulation –57. Thes studies , 13, 22  studies , 53. Howilibrium –61. In tilibrium –75. The Pro allele of the P53 gene are more likely to have breast cancer with dominant and additive models than individuals harboring the wild-type Arg allele. Our study further strengthened and confirmed the hypothesis that the P53 gene is usually mutated in about half of breast cancer cases. For the stability and homogeneity of results from meta-analysis, future similar studies should take into account selection criteria for articles such as no deviation from HWE in the control group and the matching of cases and controls according to age.In the light of this meta-analysis, we noticed that individuals carrying at least one Additional file 1. Availability of all data and references with PubMed accession numbers.