Association of Metabolic Health and Central Obesity With the Risk of Thyroid Cancer: Data from the Korean Genome and Epidemiology Study

Association of metabolic obesity phenotypes with risk of overall and site-specific cancers: a systematic review and meta-analysis of cohort studies

BACKGROUND

Adiposity is a known risk factor for certain cancers; however, it is not clear whether the risk of cancer differs between individuals with high adiposity but different metabolic health status. The aim of this systematic literature review and meta-analysis of cohort studies was to evaluate associations between metabolic obesity phenotypes and overall and site-specific cancer risk.

METHODS

PubMed and Embase databases were used to identify relevant cohort studies up to the 6th of June 2023. Random-effects models were used to estimate summary relative risks (SRRs) and 95% confidence intervals (CIs) for the association between metabolic obesity phenotypes and cancer risk. Certainty of evidence was assessed using the Cochrane methods and the GRADE tool. This study is registered with PROSPERO, number CRD42024549511.

RESULTS

A total of 15,556 records were screened, and 31 publications covering 15 unique cohort studies were included in this analysis. Of these studies, 22 were evaluated as being at low risk of bias and 9 at moderate risk of bias. Compared to metabolically healthy normal-weight individuals (MHNW), metabolically unhealthy overweight/obese (MUOW/OB) individuals had a higher risk of overall (SRR = 1.21, 95% CI = 1.02-1.44, n = 3 studies, high certainty) and obesity-related cancers (SRR = 1.42, 95% CI = 1.15-1.74, n = 3, very low certainty). Specifically, MUOW/OB individuals were at higher risk of cancers of the postmenopausal breast (SRR = 1.32, 95% CI = 1.17-1.48, n = 7, low certainty), colorectum (SRR = 1.24, 95% CI = 1.16-1.31, n = 6, moderate certainty), endometrium (SRR = 2.31, 95% CI = 2.08-2.57, n = 4, high certainty), thyroid (SRR = 1.42, 95% CI = 1.29-1.57, n = 4, moderate certainty), kidney (SRR = 1.71, 95% CI = 1.40-2.10, n = 3, low certainty), pancreas (SRR = 1.35, 95% CI = 1.24-1.47, n = 3, high certainty), liver (SRR = 1.81, 95% CI = 1.36-2.42, n = 2, moderate certainty), gallbladder (SRR = 1.42, 95% CI = 1.17-1.73, n = 2, high certainty), bladder (SRR = 1.36, 95% CI = 1.19-1.56, n = 2, moderate certainty), and stomach (SRR = 1.50, 95% CI = 1.12-2.01, n = 2, high certainty). In addition, we found elevated risks of most of these cancers among individuals classified as MUNW and MHOW/OB phenotypes compared to those with MHNW phenotype. Our stratified analyses according to metabolic obesity phenotypes suggested that the elevated risks of some cancers were stronger in individuals with MUOW/OB versus those with MHOW/OB or MUNW phenotypes.

CONCLUSION

These findings suggest that both higher adiposity and metabolic dysfunction were independently associated with increased risk of several cancers, with the strongest associations generally observed among those with both metabolic dysfunction and obesity.

The impact of diabetes, hypercholesterolemia, and low-density lipoprotein (LDL) on the survival of cervical cancer patients

BACKGROUND

Cervical cancer is a prevalent malignancy and an important health concern worldwide. Recent research has highlighted the potential impact of metabolic factors, such as hyperlipidemia and diabetes, on cancer progression, increased mortality, and patient outcomes. However, insufficient data have been reported regarding their relationship with cervical cancer. This study aimed to investigate the relationships between metabolic disorders, including dyslipidemia, dysglycemia, and metabolic syndrome, and survival in patients with cervical cancer.

METHODS

We retrospectively analyzed demographic information, clinical characteristics, and metabolic health indicators of patients with cervical cancer. Patients were categorized into groups based on specific metabolic conditions: high triglyceride, high low-density lipoprotein, high cholesterol, and diabetes groups. Additionally, the presence of metabolic syndrome and other metabolic comorbidities was recorded. The log-rank test was used to compare survival rates between different patient groups and identify associated risk factors. Survival curves generated via the Cox proportional hazards model were used to evaluate the associations between metabolic parameters and survival.

RESULTS

The Cox proportional hazards model was used to analyze data from 840 patients with cervical cancer between 28 and 72 years old who underwent surgery. The hazard ratio (HR) of mortality was 1.804 (95% CI 1.394-2.333, p < 0.001) in the high-density lipoprotein group, 0.758 (95% CI 0.558 to 1.030, p < 0.001) in the high-triglyceride group, 1.794 (95% CI 1.304-2.470, p < 0.001) in the high low-density lipoprotein group, and 0.011 (95% CI 0.005-0.025, p < 0.001) in the diabetes group. These factors were significantly associated with reduced survival in patients with cervical cancer, and these associations persisted after adjusting for age, cancer stage, treatment type, and the presence of metabolic syndrome or other comorbidities.

CONCLUSION

This study highlights the importance of metabolic health and the significance of controlling metabolic disorders, including hyperlipidemia, diabetes, and metabolic syndrome, to improve survival outcomes in patients with cervical cancer. Future research should explore the impact of managing multiple metabolic conditions on the prognosis of these patients.

Associations Between Metabolic Obesity Phenotypes and Pathological Characteristics of Papillary Thyroid Carcinoma

OBJECTIVE

The association between obesity, metabolic dysregulation, and the aggressive pathological traits of papillary thyroid carcinoma (PTC) continues to be a contentious issue. To date, no investigations have examined the impact of metabolic status on the malignant pathological features of PTC in relation to obesity.

METHODS

This research involved 855 adult patients with PTC from Shandong Provincial Hospital, classified into 4 groups based on metabolic and obesity status: metabolically healthy nonobese, metabolically unhealthy nonobese (MUNO), metabolically healthy obese, and metabolically unhealthy obese. We employed logistic regression to investigate the relationship between these metabolic obesity phenotypes and PTC's pathological characteristics. Mediation analysis was also performed to determine metabolic abnormalities' mediating role in the nexus between obesity and these characteristics.

RESULTS

Relative to metabolically healthy nonobese individuals, the metabolically unhealthy obese group was significantly associated with an elevated risk of larger tumor sizes and a greater number of tumor foci in PTC. Mediation analysis indicated that obesity directly influences tumor size, whereas its effect on tumor multifocality is mediated through metabolic dysfunctions. Specifically, high-density lipoprotein cholesterol levels were notably associated with tumor multifocality within obese subjects, serving as a mediator in obesity's impact on this trait.

CONCLUSION

The concurrent presence of obesity and metabolic dysregulation is often connected to more aggressive pathological features in PTC. The mediation analysis suggests obesity directly affects tumor size and indirectly influences tumor multifocality via low high-density lipoprotein cholesterol levels.

Obesity and thyroid cancer: unraveling the connection through a systematic review and meta-analysis of cohort studies

Background

The relationship between adiposity indicators and thyroid cancer (TC) risk has garnered increasing attention due to the rising prevalence of obesity and its potential impact on cancer incidence. We conducted a comprehensive meta-analysis to investigate this association across various effect measures.

Method

Until July 2022, a comprehensive search of databases was conducted to identify cohort studies that assessed the association between adiposity and the development of TC. Meta-analysis was performed using random effects models. Subgroup analyses were conducted to explore heterogeneity. Publication bias was assessed using Begg's tests.

Results

A systematic literature search identified 27 eligible studies reporting odds ratios (OR), relative risks (RR), or hazard ratios (HR) as effect measures. Pooling the studies irrespective of the effect measure, a significant positive association between adiposity indicators and TC risk was observed, yielding an effect estimate of 1.16 (95% CI 1.12-1.21). The combined effect estimate for OR/RR studies was 1.10 (95%CI 1.04-1.17), while HR studies yielded an effect estimate of 1.20 (95%CI 1.13-1.26). Subgroup analyses revealed associations across different age groups, obesity indices, and regions, with some variations based on effect measure. Meta-regression identified follow-up duration as a confounding factor only in HR studies.

Conclusion

The synthesis of 27 studies with diverse designs and populations underscores a robust positive association between adiposity and TC risk, providing compelling evidence for the potential role of increased adiposity in TC development.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01425-3.

Role of body mass index and weight change in the risk of cancer: A systematic review and meta-analysis of 66 cohort studies

Background

This study was designed to evaluate the effects of body mass index (BMI) and weight change on the risk of developing cancer overall and cancer at different sites.

Methods

We searched PubMed and other databases up to July 2023 using the keywords related to 'risk', 'cancer', 'weight', 'overweight', and 'obesity'. We identified eligible studies, and the inclusion criteria encompassed cohort studies in English that focused on cancer diagnosis and included BMI or weight change as an exposure factor. Multiple authors performed data extraction and quality assessment, and statistical analyses were carried out using RevMan and R software. We used random- or fixed-effects models to calculate the pooled relative risk (RR) or hazard ratio along with 95% confidence intervals (CIs). We used the Newcastle-Ottawa Scale to assess study quality.

Results

Analysis included 66 cohort studies. Compared to underweight or normal weight, overweight or obesity was associated with an increased risk of endometrial cancer, kidney cancer, and liver cancer but a decreased risk of prostate cancer and lung cancer. Being underweight was associated with an increased risk of gastric cancer and lung cancer but not that of postmenopausal breast cancer or female reproductive cancer. In addition, weight loss of more than five kg was protective against overall cancer risk.

Conclusions

Overweight and obesity increase the risk of most cancers, and weight loss of >5 kg reduces overall cancer risk. These findings provide insights for cancer prevention and help to elucidate the mechanisms underlying cancer development.

Registration

Reviewregistry1786.

Obesity and thyroid cancer risk

PURPOSE OF REVIEW

This review explores recent evidence assessing the relationship between obesity and thyroid cancer.

RECENT FINDINGS

Consistent evidence from observational studies suggests that obesity increases the risk of thyroid cancer. The relationship persists when alternative measures of adiposity are used, but the strength of association may vary according to the timing and duration of obesity and how obesity or other metabolic parameters are defined as exposures. Recent studies have reported an association between obesity and thyroid cancers that are larger or have adverse clinicopathologic features, including those with BRAF mutations, thus providing evidence that the association is relevant for clinically significant thyroid cancers. The underlying mechanism for the association remains uncertain but may be driven by disruption in adipokines and growth-signaling pathways.

SUMMARY

Obesity is associated with an increased risk of thyroid cancer, although further research is required to understand the biological mechanisms underpinning this relationship. Reducing the prevalence of obesity is predicted to lessen the future burden of thyroid cancer. However, the presence of obesity does not impact current recommendations for screening or management of thyroid cancer.

A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers

Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.

Do metabolic factors increase the risk of thyroid cancer? a Mendelian randomization study

Background

Epidemiological studies emphasize the link between metabolic factors and thyroid cancer. Using Mendelian randomization (MR), we assessed the possible causal impact of metabolic factors on thyroid cancer for the first time.

Methods

Summary statistics for metabolic factors and thyroid cancer were obtained from published Genome-wide association studies. The causal relationships were assessed using the inverse-variance weighted (IVW) method as the primary method through a two-sample Mendelian Randomization (MR) analysis. To account for the potential existence of horizontal pleiotropy, four additional methods were employed, including Mendelian Randomization-Egger (MR-Egger), weighted median method (WM), simple mode, and weighted mode method. Given the presence of interactions between metabolic factors, a multivariable MR analysis was subsequently conducted.

Results

The results showed there was a genetic link between HDL level and protection effect of thyroid cancer using IVW (OR= 0.75, 95% confidence intervals [CIs] 0.60-0.93, p=0.01) and MR-Egger method (OR= 0.70, 95% confidence intervals [CIs] 0.50- 0.97, p=0.03). The results remained robust in multivariable MR analysis for the genetic link between HDL level and protection effect of thyroid cancer (OR= 0.74, 95% confidence intervals [CIs] 0.55-0.99, p=0.04).

Conclusions

This study suggests a protection role for HDL on thyroid cancer. The study findings provide evidence for the public health suggestion for thyroid cancer prevention. HDL's potential as a pharmacological target needs further validation.

Metabolic obesity phenotypes and obesity-related cancer risk in the National Health and Nutrition Examination Survey

INTRODUCTION

Body mass index (BMI) fails to identify up to one-third of normal weight individuals with metabolic dysfunction who may be at increased risk of obesity-related cancer (ORC). Metabolic obesity phenotypes, an alternate metric to assess metabolic dysfunction with or without obesity, were evaluated for association with ORC risk.

METHODS

National Health and Nutrition Examination Survey participants from 1999 to 2018 (N = 19,500) were categorized into phenotypes according to the metabolic syndrome (MetS) criteria and BMI: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO) and metabolically unhealthy overweight/obese (MUO). Adjusted multivariable logistic regression models were used to evaluate associations with ORC.

RESULTS

With metabolic dysfunction defined as ≥1 MetS criteria, ORC cases (n = 528) had higher proportions of MUNW (28.2% vs. 17.4%) and MUO (62.6% vs. 60.9%) phenotypes than cancer-free individuals (n = 18,972). Compared with MHNW participants, MUNW participants had a 2.2-times higher ORC risk [OR (95%CI) = 2.21 (1.27-3.85)]. MHO and MUO participants demonstrated a 43% and 56% increased ORC risk, respectively, compared to MHNW, but these did not reach statistical significance [OR (95% CI) = 1.43 (0.46-4.42), 1.56 (0.91-2.67), respectively]. Hyperglycaemia, hypertension and central obesity were all independently associated with higher ORC risk compared to MHNW.

CONCLUSIONS

MUNW participants have a higher risk of ORC than other abnormal phenotypes, compared with MHNW participants. Incorporating metabolic health measures in addition to assessing BMI may improve ORC risk stratification. Further research on the relationship between metabolic dysfunction and ORC is warranted.

Evaluation of Mitochondrial Function in Blood Samples Shows Distinct Patterns in Subjects with Thyroid Carcinoma from Those with Hyperplasia

Metabolic adaptations are a hallmark of cancer and may be exploited to develop novel diagnostic and therapeutic tools. Only about 50% of the patients who undergo thyroidectomy due to suspicion of thyroid cancer actually have the disease, highlighting the diagnostic limitations of current tools. We explored the possibility of using non-invasive blood tests to accurately diagnose thyroid cancer. We analyzed blood and thyroid tissue samples from two independent cohorts of patients undergoing thyroidectomy at the Hospital Universitario 12 de Octubre (Madrid, Spain). As expected, histological comparisons of thyroid cancer and hyperplasia revealed higher proliferation and apoptotic rates and enhanced vascular alterations in the former. Notably, they also revealed increased levels of membrane-bound phosphorylated AKT, suggestive of enhanced glycolysis, and alterations in mitochondrial sub-cellular distribution. Both characteristics are common metabolic adaptations in primary tumors. These data together with reduced mtDNA copy number and elevated levels of the mitochondrial antioxidant PRX3 in cancer tissue samples suggest the presence of mitochondrial oxidative stress. In plasma, cancer patients showed higher levels of cfDNA and mtDNA. Of note, mtDNA plasma levels inversely correlated with those in the tissue, suggesting that higher death rates were linked to lower mtDNA copy number. In PBMCs, cancer patients showed higher levels of PGC-1α, a positive regulator of mitochondrial function, but this increase was not associated with a corresponding induction of its target genes, suggesting a reduced activity in cancer patients. We also observed a significant difference in the PRDX3/PFKFB3 correlation at the gene expression level, between carcinoma and hyperplasia patients, also indicative of increased systemic metabolic stress in cancer patients. The correlation of mtDNA levels in tissue and PBMCs further stressed the interconnection between systemic and tumor metabolism. Evaluation of the mitochondrial gene ND1 in plasma, PBMCs and tissue samples, suggested that it could be a good biomarker for systemic oxidative metabolism, with ND1/mtDNA ratio positively correlating in PBMCs and tissue samples. In contrast, ND4 evaluation would be informative of tumor development, with ND4/mtDNA ratio specifically altered in the tumor context. Taken together, our data suggest that metabolic dysregulation in thyroid cancer can be monitored accurately in blood samples and might be exploited for the accurate discrimination of cancer from hyperplasia.

Metabolic syndrome and thyroid Cancer: risk, prognosis, and mechanism

The increasing incidence of thyroid cancer (TC) cannot be fully explained by overdiagnosis. Metabolic syndrome (Met S) is highly prevalent due to the modern lifestyle, which can lead to the development of tumors. This review expounds on the relationship between Met S and TC risk, prognosis and its possible biological mechanism. Met S and its components were associated with an increased risk and aggressiveness of TC, and there were gender differences in most studies. Abnormal metabolism places the body in a state of chronic inflammation for a long time, and thyroid-stimulating hormones may initiate tumorigenesis. Insulin resistance has a central role assisted by adipokines, angiotensin II, and estrogen. Together, these factors contribute to the progression of TC. Therefore, direct predictors of metabolic disorders (e.g., central obesity, insulin resistance and apolipoprotein levels) are expected to become new markers for diagnosis and prognosis. cAMP, insulin-like growth factor axis, angiotensin II, and AMPK-related signaling pathways could provide new targets for TC treatment.

Risk factors and diagnostic prediction models for papillary thyroid carcinoma

Thyroid nodules (TNs) represent a common scenario. More accurate pre-operative diagnosis of malignancy has become an overriding concern. This study incorporated demographic, serological, ultrasound, and biopsy data and aimed to compare a new diagnostic prediction model based on Back Propagation Neural Network (BPNN) with multivariate logistic regression model, to guide the decision of surgery. Records of 2,090 patients with TNs who underwent thyroid surgery were retrospectively reviewed. Multivariate logistic regression analysis indicated that Bethesda category (OR=1.90, P<0.001), TIRADS (OR=2.55, P<0.001), age (OR=0.97, P=0.002), nodule size (OR=0.53, P<0.001), and serum levels of Tg (OR=0.994, P=0.004) and HDL-C (OR=0.23, P=0.001) were statistically significant independent differentiators for patients with PTC and benign nodules. Both BPNN and regression models showed good accuracy in differentiating PTC from benign nodules (area under the curve [AUC], 0.948 and 0.924, respectively). Notably, the BPNN model showed a higher specificity (88.3% vs. 73.9%) and negative predictive value (83.7% vs. 45.8%) than the regression model, while the sensitivity (93.1% vs. 93.9%) was similar between two models. Stratified analysis based on Bethesda indeterminate cytology categories showed similar findings. Therefore, BPNN and regression models based on a combination of demographic, serological, ultrasound, and biopsy data, all of which were readily available in routine clinical practice, might help guide the decision of surgery for TNs.