Retinoid cancer

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Traffic lights for retinoids in oncology: molecular markers of retinoid resistance and sensitivity and their use in the management of cancer differentiation therapy

BMC Cancervolume 18, Article number: 1059 (2018) Cite this article

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Abstract

For decades, retinoids and their synthetic derivatives have been well established anticancer treatments due to their ability to regulate cell growth and induce cell differentiation and apoptosis. Many studies have reported the promising role of retinoids in attaining better outcomes for adult or pediatric patients suffering from several types of cancer, especially acute myeloid leukemia and neuroblastoma. However, even this promising differentiation therapy has some limitations: retinoid toxicity and intrinsic or acquired resistance have been observed in many patients. Therefore, the identification of molecular markers that predict the therapeutic response to retinoid treatment is undoubtedly important for retinoid use in clinical practice. The purpose of this review is to summarize the current knowledge on candidate markers, including both genetic alterations and protein markers, for retinoid resistance and sensitivity in human malignancies.

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Introduction

Defective or aberrant cell differentiation is a hallmark of many human malignancies. The initial step in an aberrant tumor cell phenotype involves various mutations that alter signaling pathways, epigenetic modifiers, and transcription factors, leading to the deregulated expression of proteins required for cell differentiation.

During the 1970s and 1980s, as an elegant alternative to killing cancer cells by cytotoxic therapies, several scientific achievements popularized the strategy of inducing malignant cells to overcome differentiation inhibition and to enter apoptotic pathways [1]. The initial preclinical results proved to be very promising and fueled hope for the development of a new approach in cancer treatment called “differentiation therapy” [2].

In general, differentiation therapy aims to reactivate the endogenous differentiation program in transformed cells to resume the mutation process and eliminate the tumor phenotype. Thus, this strategy offers the prospect of a less aggressive treatment that limits damage to the normal cells in the organism.

Natural and synthetic retinoids in anticancer treatment

Retinoids, i.e., natural and synthetic vitamin A derivatives, have been studied for decades in clinical trials due to their established role in regulating cell growth, differentiation and apoptosis. Retinoids are key compounds in biological differentiation therapy. Retinoids have critical functions in many aspects of human biology: at the cellular level, they control cell differentiation, growth, and apoptosis [3]. Several biologically active vitamin A derivatives, namely, all-trans retinoic acid (ATRA), 9-cis retinoic acid (9-cis-RA), and 13-cis retinoic acid (13-cis-RA), have been tested for potential use in cancer therapy and chemoprevention [4,5,6,7]. The most effective clinical use of ATRA was demonstrated in acute promyelocytic leukemia (APL) treatment [8]. Additional studies have indicated that 13-cis-RA is beneficial in high-risk neuroblastoma (NBL) treatment after bone marrow transplantation, suggesting that retinoids may play an adjuvant therapeutic role in the management of minimal residual disease [9]. List of all human malignancies, for which the clinical treatment with retinoids was already tested, is given in the Table 1.

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Nevertheless, vitamin A-associated toxicity involving liver and lipid alterations, dry skin, teratogenicity, bone and connective tissue damage substantially limits the long-term administration of natural retinoids. Both ATRA and 13-cis RA are pan-RAR activators, which can explain their large negative side effects. For these reasons, the modification of several functional groups has produced new, synthetic retinoids that have increased chemoprevention efficacy and reduced toxicity compared with these parameters in other natural retinoids. These modifications include the substitution of benzoic acid with aromatic rings or can change their solubility in water, for example. Fenretinide (N-(4-hydroxyphenyl) retinamide, 4-HPR) has been discussed as an effective cancer treatment, especially due to its pro-apoptotic and anti-angiogenic effects even in ATRA-resistant cell lines and with minor side-effects profile [10]. Bexarotene is a synthetic retinoid that is approved by the European Medicines Agency to treat skin manifestations of advanced-stage cutaneous T-cell lymphoma in adult patients refractory to at least one systemic treatment [11]. Several studies have suggested that bexarotene is an effective anticancer treatment that is able to decrease proliferation and promote apoptosis in cells expressing retinoid X receptors (RXRs) [12, 13]. A very recent study described synthesis of a novel retinoid WYC-209, which abrogates growth of melanoma tumor-repopulating cells and inhibits lung metastases in vivo, showing minimal toxicity on non-tumor cells [14].

When it comes to synthetic RA analogues that are still being synthesized and tested, the biggest disadvantage of such new compounds is undoubtedly the lack of information about their long-term effects on human body.

Mechanisms of retinoid resistance

Biological retinoid activity is based on the binding of retinoids to specific nuclear receptors (retinoic acid receptors (RARs) bind retinoic acid and RXRs bind retinoids) that act as inducible transcription factors. When activated, these nuclear receptors form RXR-RAR heterodimers or RXR-RXR/RAR-RAR homodimers that subsequently modulate retinoid-responsive gene expression two ways: (i) by binding to retinoic acid response elements (RAREs) in the promoter regions of target genes or (ii) by antagonizing the enhancer action of other transcription factors, such as AP1 or NF-IL6 [15].

Although pharmacological retinoid doses have been approved by the Food and Drug Administration (FDA) and other regulatory bodies for the treatment of some hematologic malignancies and high-risk NBL, the chemopreventive and therapeutic effects of retinoids in other solid tumors are still unclear. Even in tumors that are treated with retinoids the therapeutic response to the retinoids is often limited to a small proportion of the treated patients [16]. This limited effect is thought to result from retinoid resistance, which is defined as the lack of a tumor cell response to the same pharmacological dose of retinoids that sensitive cells respond to, as evidenced by proliferation arrest or differentiation. Moreover, after retinoid treatment, some carcinomas not only fail to exhibit growth inhibition but instead respond with enhanced proliferation. A clue to this paradoxical behavior was suggested by the finding that retinoic acid and its natural receptor also activate peroxisome proliferator-activated receptor (PPAR) β and δ (PPARβ/δ), which are involved in mitogenic and anti-apoptotic activities [17].

Many potential mechanisms have been proposed for retinoid resistance (Fig. 1). In general, the cancer cell response to the pharmacological retinoid doses is affected by several mechanisms, including decreased retinoid uptake [18], increased retinoid catabolism by cytochrome P450 [19], active drug efflux by membrane transporters, the downregulated expression of various RAR genes (promoter methylation), the altered expression of coactivators or downstream target genes, and changes in the activities of other signaling pathways [20].

Possible mechanisms of retinoid resistance. Cancer cell retinoid resistance may be caused by several independent mechanisms including (1) decreased retinoid uptake; (2) intracellular retinoid metabolism; (3) altered intracellular retinoid availability due to CRAB protein binding; (4) increased retinoid efflux by ABC transporters; (5) increased retinoid catabolism catalyzed by cytochrome P450; (6) decreased RAR and/or RXR expression; (7) inhibited retinoid-induced transcription by the repressor complex, (8) altered coactivator structure, expression, or activity; (9) altered downstream target gene expression

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Although retinoid resistance remains problematic in the area of biological anticancer therapy, the discovery of biomarkers that indicate retinoid resistance or sensitivity in each individual patient seems to be important for the recent personalized therapy strategy, which is aimed at identifying of the most effective therapy for individual patients. In the next chapters, we focus on describing the most promising putative biomarkers that predict retinoid resistance or sensitivity in the most relevant cancer types.

Predictive biomarkers of retinoid resistance

During the past decades, several biomarkers have been identified that can predict the therapeutic response to retinoid treatment in a few human malignancies, including adult leukemia, pancreatic and breast carcinoma and pediatric NBL. These predictive biomarkers are both genetic alterations (typically chromosomal translocations leading to fusion protein expression) and proteins (upregulated or downregulated). In the following parts of this review, we present the recent knowledge concerning these biomarkers in relation to retinoid resistance and sensitivity. An overview of all these biomarkers is given in the Table 2.

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Predictive biomarkers in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogenous malignant clonal disease characterized by the accumulation of undifferentiated myeloid blasts, which predisposes patients, especially those with APL-type AML, to overcome impaired differentiation via differentiation-inducing agents, such as granulocyte-colony stimulating factor (GCSF) or ATRA, in addition to conventional chemotherapy [21, 22]. Despite providing high cure rates, such approach is associated with hematologic toxicity as well as with the risk of secondary myeloid neoplasms in approximately 2% of patients. The introduction of arsenic trioxide (ATO) and especially the studies on combined treatment with ATRA plus ATO showed the possibility how to improve the effectiveness of ATRA in APL patients: two large independent randomized trials reported significant improvement in clinical outcome of patients treated with ATRA-ATO if compared with those receiving ATRA only [23, 24].

Studies from the last decade identified meningioma 1 (MN1) as a hematopoietic oncogene with a key role in myeloid leukemogenesis. Based on the gene expression analyses in several hundreds of AML patients, MN1 overexpression is associated with a poor prognosis in these patients [25,26,27]. Specifically, 67.4% AML patients had high levels of MN1 expression if compared with control group and 75% of AML patients with high MN1 expression were classified as of intermediate risk according cytogenetic risk categories [27]. The MN1 protein seems to have at least two functions: promote self-renewal and proliferation and block cell differentiation [28]. Interestingly, MN1 locates to RAREs and has been implicated as a transcription cofactor in RAR-RXR-mediated transcription [29]. A study on the MN1 expression pattern in AML patients revealed that MN1 overexpression is strongly associated with resistance to ATRA-induced differentiation and cell cycle arrest. In MN1-overexpressing hematopoietic cells, several genes regulated by RARα (p21, p27) were repressed and were not upregulated by ATRA treatment [28].

APL is also characterized by a specific chromosomal translocation (Fig. 2a) between the retinoic acid receptor alpha (RARA) and a number of fusion partners (X-RARA). This chromosomal rearrangement plays a critical role in the disease phenotype, particularly regarding ATRA sensitivity. Although a high proportion of APL patients achieve complete remission after treatment with ATRA, most patients who receive continuous ATRA treatment later relapse and develop the ATRA-resistant phenotype of this disease [30]. At least 98% of APL patients carry the t(15;17) translocation, resulting in RARA fusion with the promyelocytic leukemia (PML) gene (PML-RARA) [31]. The fusion of PML sequences to RARA regions increases fusion receptor affinity for co-repressors [32]. Therefore, the increased levels of ATRA are required to induce dissociation of co-repressors and to promote a therapeutic response to the treatment. In addition to PML, a limited number of patients exhibit a variety of other X-RARA fusions [33,34,35,36,37,38,39]. The fusion partner also plays a key role in the response to the retinoid treatment: APL patients carrying NPM1 and NuMA fusion partners respond clinically to ATRA treatment [40, 41], whereas APL cases involving PLZF (promyelocytic leukemia zinc finger), IRF2BP2 (interferon regulatory protein 2 binding protein 2) and STAT5b presented with ATRA resistance and a poor prognosis [42,43,44,45]. One of the most important tools in APL treatment is minimal residual disease monitoring with a special focus on the molecular detection of the PML-RARA transcript. Although the possibility of this monitoring was also reported in patients with PLZF-RARA- and STAT5b-RARA-positive diseases, no data regarding the clinical value of this tool are available [46, 47].

Genetic alterations used as predictive biomarkers for APL patients. a Chromosomal translocations between RARA and several fusion partners playing an important role in maintaining resistance/sensitivity of APL patients to retinoids [122]. b Breakpoint cluster regions (bcr) in PML gene resulting in alternative splicing and different therapeutic response to ATRA in APL patients. E5(−)E6(−) isoform of L-type fusion transcript with exons 5 and 6 deleted is associated with the ATRA-resistant phenotype

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Molecular analysis of the possible mechanisms of retinoid resistance suggested that the reciprocal RARA-PLZF fusion product from the derivative chromosome 17 [der(17)] functions as a transcriptional activator targeting PLZF-binding sites, leading to cellular retinoic acid-binding protein 1 (CRABP1) upregulation. The CRABP1 protein is structurally similar to the cellular retinol-binding proteins, sequesters retinoic acid to limit its access to the nucleus [48], and is a well-established mediator of retinoid resistance in various biological models [49,50,51]. Similarly, APL patients expressing both fusion gene products exhibited primary resistance to ATRA [42, 52, 53]. In contrast, blast cells from a patient with the PLZF-RARA fusion transcript only were sensitive to ATRA treatment under in vitro conditions, and these results correlated with clinical remission after ATRA administration in this patient [54]. Moreover, two fusion proteins, PLZF-RARA and RARA-PLZF, negatively impacted the activity of CCAAT/enhancer binding protein α (C/EBPα), a master regulator of granulocytic differentiation [55]. Further research in a murine APL model demonstrated that the co-administration of 8-CPT-cAMP (8-chlorophenylthio-adenosine-3′, 5′-cyclic monophosphate) improves the therapeutic effect of ATRA by enhancing cellular differentiation and increasing PLZF-RARA degradation [56]. Nevertheless, the ability of this type of combined differentiation therapy to overcome retinoid resistance has never been proven in humans.

Published results on APL cell lines also suggest a possible association between the splicing variants of the PML-RARA fusion gene and the therapeutic response to ATRA [57]. These variants resulted from the alternative splicing of the PML sequence, which contains heterogeneous breakpoint cluster regions (bcrs) at three different sites (Fig. 2b) [58,59,60].

Sequencing analysis of the PML-RARA gene in a cohort of 79 APL patients showed that the L-type fusion transcript resulting from the alternative splicing was present in three isoforms. One of these isoforms, the E5(−)E6(−) isoform with exons 5 and 6 deleted, is associated with the ATRA-resistant phenotype [57]. A subsequent localization study reported that the E5(−)E6(−) protein was detected in the cytoplasm only, whereas the other two isoforms were distributed throughout the nucleus and cytoplasm. The exclusive cytoplasmic localization of the E5(−)E6(−) isoform is apparently responsible for inhibiting ATRA-dependent transcription and for subsequently blocking cell differentiation. Thus, monitoring E5(−)E6(−) isoform expression in APL patients with the L-type PML-RARA fusion gene might be helpful for predicting a patient’s response to ATRA treatment.

Similarly, APL cells with the V-type splicing isoform, characterized by exon 6 truncation, were also reported to be less sensitive to ATRA treatment. In this group of APL patients, a subset with lower ATRA sensitivity presented with a relatively long “spacer” with a cryptic coding sequence inserted into the joining sites between the truncated PML and RARA mRNA fusion partners. Subsequent in vitro studies confirmed these results, revealing that spacer deletion restored ATRA sensitivity [61].

Predictive biomarkers in pancreatic ductal adenocarcinoma

The vitamin A metabolism disturbances that result in a decreased intracellular ATRA concentration were originally described in pancreatic ductal adenocarcinoma (PDAC) [62] and later, in other human malignancies, also [63]. Previous studies in PDAC cell lines have indicated the ability of ATRA to induce cell cycle arrest and differentiation, although these data revealed highly variable retinoid sensitivity among the PDAC cell lines [64, 65]. Based on the receptor-dependent retinoid mechanism, the potential patient benefit from this treatment is highly dependent on the retinoid receptor expression level in tumor tissue. Among others, RARβ expression is downregulated in PDAC [66,67,68], which may explain the negative outcomes of clinical trials focused on retinoid treatments.

ATRA typically induces cell differentiation and growth arrest in most epithelial cell types. However, experiments in Capan-1 cell line have shown that in addition to an antiproliferative effect, retinoids increase cell migration, resulting in an invasive phenotype [69]. This effect is probably caused by the presence of the nuclear receptors PPARβ/δ, which are also activated by exogenous retinoids and form heterodimers with RXR. While canonical RAR-dependent gene expression leads to growth arrest, PPARβ/δ activation initiates proliferation, cell survival and tumor growth in mouse model [70]. The distribution of available ATRA between PPARβ/δ and RAR receptors is regulated by the levels of two key intracellular ligand-binding proteins: fatty acid-binding protein 5 (FABP5) and cellular retinoic acid-binding protein 2 (CRABP2). Depending on their relative abundance within the cell, FABP5 and CRABP2 transport exogenous retinoids from the cell cytoplasm into the nucleus, to either PPARβ/δ or RARs [17]. A recent study on 14 PDAC cell lines demonstrated that it might be possible to predict PDAC cell sensitivity to ATRA on the basis of the relative expression levels of these two retinoid-binding proteins. According to this study, 10 of 14 cell lines expressed the one or the other binding protein confirming the pattern of reciprocal differential expression of both transcripts in PDAC cells. FABP5highCRABP2null PDAC lines were resistant to ATRA-mediated growth inhibition and apoptosis and also exhibited an increased migration and invasion phenotype. In contrast, FABP5nullCRABP2high cell lines retained ATRA sensitivity. These results were also confirmed in vivo using xenograft models [71]. Immunohistochemical detection of FABP5 in PDAC samples revealed that about 20% of them were completely negative for FABP5 indicating these patients as suitable candidates for retinoid therapy [71]. Since the retinoid binding affinity of the CRABP2-RAR pathway is higher than that of the FABP5-PPARβ/δ pathway, at least a partial ATRA-mediated tumor-suppressive effect is expected in tumors with comparable FABP5 and CRABP2 expression.

Predictive biomarkers in breast carcinoma

Breast carcinoma is a heterogenous disease classified into subtypes according to the expression of biological markers, such as estrogen receptor (ER), progesterone receptor (PR) and epidermal growth factor receptor 2 (HER2) [72,73,74]. According to recent clinical trials aimed at investigating the efficacy of retinoids as adjuvant treatment in breast carcinoma, some patients benefited from the retinoid treatment. Moreover, the breast carcinoma cell response to retinoids can be predicted by evaluating the expression of several marker proteins.

Indeed, several studies have demonstrated that the average RARα receptor level is significantly higher in ATRA-sensitive than ATRA-resistant breast carcinoma cell lines [75,76,77]. Furthermore, a truncated RARβ’ isoform has also been identified in some of these cell lines and it has been associated with increased cell proliferation and ATRA resistance [78].

Another potential marker of ATRA resistance was suggested by a study describing Her2/neu-induced ATRA resistance in breast cancer cell lines [79]. ERBB2 transfection in ATRA-sensitive breast carcinoma cells induced ATRA resistance. When Her2/neu was blocked by trastuzumab, the cells exhibiting induced ATRA resistance became ATRA sensitive again. This study also hypothesized that Her2/neu may induce ATRA resistance in breast carcinoma cells by suppressing RARA expression and/or by deregulating the G1 checkpoint of the cell cycle.

As described in the PDAC section in this review, the abundance of the intracellular retinoic acid transporters CRABP2 and FABP5 within the cell can indicate breast carcinoma cell response to ATRA, since these molecules have been shown to play opposing roles in mediating the cellular response to retinoids [17]. According to the microarray analysis of gene expression in 176 primary breast carcinoma samples, FABP5 is preferentially upregulated in estrogen receptor-negative (ER-) and triple-negative breast carcinoma cells (TNBC), and an increased FABP5 mRNA level is associated with poor patient prognosis and high tumor grade [80]. In this study, FABP5 normalized signal intensity scores were categorized into high versus low using cut-off point of 0.768. In this cohort, 61% of patients showed high FABP5 expression and these patients had a significantly decreased survival rate if compared with those with low FABP5 expression. Moreover, FABP5 silencing in Hs578T breast carcinoma cell line resulted in approximately 40% reduction in proliferation activity. However, although breast cancer cells with an increased FABP5/CRABP2 ratio present with increased ATRA resistance, this ratio does not always accurately predict the breast cancer cell response to ATRA, indicating that other factors are also involved in the mechanism of retinoid resistance development. Another recent study identified CRABP1 as the third key player that potentially influences the breast cancer cell response to ATRA. This protein has been identified as a retinoid inhibitor and probably sequesters retinoic acid in the cytoplasm, thereby preventing RAR activation in the nucleus. Similarly to FABP5, CRABP1 is also preferentially expressed in ER- and TNBC tumor tissues that are prone to ATRA resistance [81]. According to this study, CRABP1 synergizes with FABP5 to compete with CRABP2 for retinoic acid molecules, thereby reducing retinoic acid access to RARs within the nucleus.

These findings provide molecular tools to predict and eventually overcome ATRA resistance in breast carcinoma therapy. CRABP1 and FABP5 co-expression may serve as a predictive biomarker of ATRA resistance in this tumor type, and the downregulation may be a key step in (re)sensitizing breast carcinoma cells to retinoid therapy. A novel mechanism for resensitizing ATRA-resistant cells to ATRA-mediated apoptosis was recently introduced: the phytochemical curcumin is able to upregulate CRABPII, RARβ and RARγ expression in TNBC cell lines and thereby sensitizes cells to ATRA-induced apoptosis. This reversed resistance to ATRA-induced apoptosis in TNBC cells was dependent on the curcumin dose and treatment length [82]. Overall, this study highlights the potential of curcumin as a possible therapeutic adjuvant in ATRA-resistant breast carcinomas.

Another recent study compared the phosphoproteome and transcriptome of established ATRA-sensitive and ATRA-resistant cell lines derived from breast carcinoma (MCF7, BT474). One of the most interesting results was that ATRA did not regulate the phosphorylation of the same proteins in both cell lines, i.e., the ATRA-resistant cell line exhibited a deregulated kinome. High-throughput sequencing experiments revealed that 80% of the genes regulated by ATRA in MCF7 cells were not regulated in BT474 cells and vice versa. Additionally, 40% more genes were regulated by ATRA in the MCF7 cells than in the BT474 cells. Moreover, this study indicates that ATRA induced RARα phosphorylation in resistant cell lines only, which may cause kinome deregulation and consequences in other intracellular metabolic pathways [83].

Predictive biomarkers in neuroblastoma

Neuroblastoma (NBL) is a neuroectodermal tumor arising from elements of the neural crest and represents the most common extracranial solid tumor in children. In a subset of high-risk NBL patients with minimal residual disease, retinoid administration was proven effective as a part of postconsolidation therapy after intensive multimodal treatment. Unfortunately, approximately 50% of this patient population is resistant to this treatment or develops resistance during therapy [84]. Moreover, a recent study evaluated the efficacy and safety of additional retinoid therapy in NBL patients and presented a more critical view, concluding that no clear evidence exists for a difference in overall survival and event-free survival in patients with high-risk NBL treated with or without retinoids [85]. However, the usefulness of differentiation therapy with retinoids largely depends on the ability to identify a subset of NBL patients who benefit from this treatment, according to analyses of retinoid resistance/sensitivity markers. Recent investigations on the mechanisms of retinoid resistance identified several downstream retinoid-regulated proteins and discussed these proteins as possible predictive biomarkers for the clinical response to retinoid treatment.

PBX1 belongs to the three-amino-acid loop extension (TALE) family of atypical homeodomain proteins and interacts with other homeodomain-containing nuclear proteins, such as HOX and MEIS, to form heterodimeric transcription complexes. PBX1 is involved in a variety of biological processes including cell differentiation and tumorigenesis [86, 87]. Recent study revealed that in NBL cell lines treated with 13-cis-RA, PBX1 mRNA and protein expression levels are both induced in 13-cis-RA-sensitive cell lines only. After treatment with 13-cis RA, all 6 RA-sensitive cell lines showed a significant increase in PBX1 expression, whereas RA-resistant cell lines exhibited no such effect. These studies also revealed that reduced PBX1 protein levels result in an aggressive growth phenotype and 13-cis-RA resistance. Finally, the authors demonstrated that in primary NBL tumor tissue, PBX1 expression correlated with the histological NBL subtype, with the highest PBX1 expression in benign ganglioneuromas and the lowest expression in high-risk NBL [88].

Homeobox (HOX) proteins function as regulators of morphogenesis and cell fate specification and are key mediators of retinoid action in nervous system development. Among members of the HOX family of transcription factors, HOXC9 seems to play an important role in neuronal differentiation. A recent study revealed that the HOXC9 promoter is epigenetically primed in an active state in ATRA-sensitive NBL cell lines and in a silenced state in ATRA-resistant NBL cell lines. Moreover, HOXC9 protein levels were significantly higher in differentiated NBL cells than in NBL cells undergoing ATRA-induced differentiation [89].

The protein neurofibromin 1 (NF1) is known to antagonize the activation of RAS proteins but is also involved in other signaling pathways, such as the cAMP/PKA pathway [90]. NF1 controls the retinoid treatment response in NBL cells through the RAS-MEK signaling cascade and has been identified as the lead candidate gene for influencing retinoic acid-induced differentiation in NBL cell models [91]. According to this study, SH-SY5Y cells with NF1 knockdown continued to proliferate when exposed to RA in contrast to the control cells. Subsequent experiments showed downregulation of RA target genes in NF1 knockdown cells. These results may indicate the role of NF1 in maintaining RA resistant phenotype.

In further research, genomic aberrations of the NF1 gene were found in 6% of primary NBL representing a subset of cases where the loss of NF1 gene could be caused by gene mutation.

A connection between NF1-RAS-MEK signaling and retinoic acid action was demonstrated by the finding that the NF1-RAS-MEK cascade suppresses ZNF423 protein expression, which functions as a RAR/RXR coactivator. Additionally, tumors with activated RAS signaling and low ZNF423 expression present with a poor response to 13-cis-RA (isotretinoin) treatment. Moreover, decreased NF1 and ZNF423 gene expression, reflecting hyperactivated RAS/MAPK signaling, is correlated with a very poor clinical outcome in NBL patients and was detected in 78% of patients with relapsed NBL [92], whereas high expression levels both of these proteins are associated with the best prognosis in NBL patients. As a result, Holzel and colleagues suggest that pharmacological MEK inhibition can sensitize NBL cells that are resistant to retinoid-induced terminal differentiation. Although these data seem to be readily translatable, several important questions will need to be addressed before incorporating this therapy into clinical practice. It will be critically important to determine how MEK inhibition combined with isotretinoin will fit into the overall NBL treatment strategy and whether MAPK pathway activation is a mechanism of acquired resistance to isotretinoin therapy or a collateral event of oncogenic driver mutations only [93]. Another recent study also indicated a potential role of MEK cascade inhibition in overcoming ATRA resistance in malignant peripheral nerve sheath tumors (MPNST) in vitro, but no correlation was found between ZNF423 mRNA levels and the sensitivity of MPNST cells to ATRA [94]. These results demonstrate that some other mechanisms are involved in maintaining ATRA resistance of MPNSTS cells.

High-mobility group A (HMGA) proteins function as ancillary transcription factors and regulate gene expression through direct DNA binding or protein-protein interactions and play important functions in controlling cell growth and differentiation. HMGA2 is completely absent in adult organisms; its expression is restricted to rapidly dividing embryonic cells and tumors with epithelial and mesenchymal origins [95]. HMGA2 was also detected in some retinoid-resistant NBL cell lines. In NBL cell lines, a causal link between HMGA2 expression and retinoid-induced growth arrest inhibition was proven using exogenous HMGA2 expression, which was sufficient to convert HMGA2-negative, retinoid-sensitive cells into retinoid-resistant cells [96]. In contrast, HMGA1 was found to be expressed at different levels in all NBL cell lines [97], indicating that its action is necessary for functions conserved throughout the developmental differentiation of the sympathetic system.

UNC45A, another potential marker of retinoid resistance, is a protein encoded by the UNC45A gene, a member of UNC45-like genes, which are evolutionarily highly conserved, and the resulting protein products are involved in muscle development and myosin assembly [98]. The UNC45A protein has been shown to modulate the HSP90-mediated molecular chaperoning of the progesterone receptor, since the UNC45A blocks the chaperoning of this receptor to the hormone-binding state [99]. In NBL cell lines, the role of UNC45A in causing ATRA resistance was suggested by Epping and co-workers [100]. When UNC45A was ectopically expressed in their experiments, ATRA-sensitive human NBL cell lines failed to undergo growth arrest after ATRA treatment. The UNC45A protein levels required for ATRA resistance were similar to the levels in several cancer cell lines. Neither the endogenous nor the ectopically expressed UNC45A protein levels were affected by ATRA treatment. Moreover, UNC45A expression also inhibited the differentiation of NBL cells cultured in the presence of ATRA, indicating the resistant phenotype.

Conclusion

This review was aimed to summarize the current knowledge, both clinical and experimental, on predictive markers in human cancers that are treated with retinoids as a part of the therapeutic regimen. This review demonstrated that each described cancer type seems to have a unique pattern of altered signaling pathways, resulting in a set of predictive biomarkers that indicate retinoid resistance or sensitivity, which is typical for this malignancy. Many of the research studies mentioned in this review are only initial, and the acquired results require further detailed investigation and clinical validation of the proposed predictive biomarkers. However, these studies demonstrate the promising future for differentiation therapies that use retinoids, especially in identifying reliable markers that predict the response of each individual patient to this type of treatment. Hopefully, the personalized approach will be a new milestone in anticancer differentiation therapy.

Abbreviations

13-cis retinoic acid

Fenretinide (N-(4-hydroxyphenyl) retinamide)

8-chlorophenylthio-adenosine-3′, 5′-cyclic monophosphate

9-cis retinoic acid

Acute myeloid leukemia

Acute promyelocytic leukemia

Arsenic trioxide

All-trans retinoic acid

Breast carcinoma

Breakpoint cluster region

CCAAT/enhancer binding protein α

Cellular retinoic acid-binding protein

Estrogen receptor

Fatty acid-binding protein 5

Food and Drug Administration

Granulocyte-colony stimulating factor

Epidermal growth factor receptor 2

High-mobility group A

Homeobox

Interferon regulatory protein 2 binding protein 2

Meningioma 1

Malignant peripheral nerve sheath tumor

Neuroblastoma

Pancreatic ductal adenocarcinoma

Promyelocytic leukemia zinc finger

Promyelocytic leukemia

Peroxisome proliferator-activated receptor

Progesterone receptor

Retinoic acid receptor

Retinoic acid response elements

Retinoid X receptor

Three-amino-acid loop extension

Triple-negative breast cancer

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Sours: https://bmccancer.biomedcentral.com/articles/10.1186/s12885-018-4966-5

Retinoids in cancer prevention and therapy

Retinoids are a class of compounds structurally related to vitamin A. In preclinical studies, all-trans retinoic acid (tretinoin), 13-cis retinoic acid (isotretinoin) and the aromatic retinoids etretinate and acitretin have preventive and therapeutic effects on carcinogen-induced premalignant and malignant lesions. Clinically, chemoprevention with isotretinoin and etretinate has been tested with some degree of success in such indications as basal cell carcinomas, squamous cell carcinomas, superficial bladder tumors and second primary tumors in patients with squamous cell carcinoma of the head and neck. Limited therapeutic success has also been achieved with retinoid treatment of precancerous and cancerous conditions of the skin, oral cavity, larynx, lung, bladder and vulva. Dramatic therapeutic effects have been observed in the treatment of acute promyelocytic leukemia with tretinoin, which leads to very high rate of complete remission. Excellent results were recently reported in the treatment of squamous cell carcinomas of the skin and cervix with a combination of isotretinoin and recombinant interferon alfa-2a (rIFN alfa-2a, Roferon-A). The mechanism of action of retinoids is through modulation of cell proliferation and differentiation. Retinoids vary in their capacity to induce differentiation and to inhibit proliferation in a series of human transformed hematopoietic and epithelial cell lines. Some cytokines potentiate the retinoid-induced cell differentiation and act synergistically with retinoids to inhibit cell proliferation. The pattern of synergism is dependent upon the combination and tumor cell line tested. The discovery of nuclear retinoid receptors has contributed substantially to the understanding of the mechanism of action of retinoids at the molecular level. Further understanding of the molecular biology of retinoids is expected to contribute to a rational design of new retinoids in the future, which in turn may result in improvements in the prevention and therapy of cancer.

Sours: https://pubmed.ncbi.nlm.nih.gov/1498071/
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The promise of retinoids to fight against cancer

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    Retinoids, Retin-A, Retinol for Anti Aging- Dr Dray Q\u0026A

    Abstract

    Retinol, the most biologically active form of vitamin A, might influence cancer-related biological pathways. However, results from observational studies of serum retinol and cancer risk have been mixed. We prospectively examined serum retinol and risk of overall and site-specific cancer in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 29,104 men), conducted in 1985–1993, with follow-up through 2012. Serum retinol concentration was measured using reverse-phase high-performance liquid chromatography. Cox proportional hazards models estimated the association between baseline serum retinol quintile and overall and site-specific cancer risk in 10,789 cases. After multivariable adjustment, higher serum retinol was not associated with overall cancer risk (highest vs. lowest quintile: hazard ratio (HR) = 0.97, 95% confidence interval (CI): 0.91, 1.03; P for trend = 0.43). Higher retinol concentrations were, however, associated with increased risk of prostate cancer (highest vs. lowest quintile: HR = 1.28, 95% CI: 1.13, 1.45; P for trend < 0.0001) and lower risk of both liver and lung cancers (highest vs. lowest quintile: for liver, HR = 0.62, 95% CI: 0.42, 0.91; P for trend = 0.004; and for lung, HR = 0.80, 95% CI: 0.72, 0.88; P for trend < 0.0001). No associations with other cancers were observed. Understanding the mechanisms that underlie these associations might provide insight into the role of vitamin A in cancer etiology.

    cancer, cohort study, prospective study, serum retinol, vitamin A

    Abbreviations

    • ATBC

      Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study

    • CI

    • HR

    • ICD-9

      International Classification of Diseases, Ninth Revision

    Retinoids are a class of synthetic and biological molecules that have chemical structures similar to vitamin A, of which retinol is the most biologically active in humans (1). Retinol is found in some foods, but much of the body’s retinol is derived from ingestion of provitamin A carotenoids which are subsequently converted to retinol. These compounds have been shown to have potentially anticarcinogenic properties such as induction of apoptosis and cellular differentiation, inhibition of proliferation, antioxidant/free radical quenching activities, and enhancement of immune surveillance (2). However, there is also evidence that retinoids might enhance tumor growth at some sites (3, 4). Thus, the multifaceted role of retinol in cancer remains unclear.

    Previous studies, examining the association between vitamin A and cancer at various sites by measuring dietary and supplemental intake of vitamin A and provitamin A carotenoids using food frequency questionnaires, have had inconsistent results. The recent World Cancer Research Fund Second Expert Report on diet and cancer judged that there was probable evidence that foods containing carotenoids protect against cancers of the head and neck and of the lung, and that foods containing β-carotene protect against cancer of the esophagus (5). However, studies have demonstrated poor correlation between dietary or supplemental intake and circulating retinol levels (6), likely due to dietary measurement error and because retinol concentrations are contributed to not only by dietary and supplemental intake but also by factors related to absorption, cleavage of provitamin A compounds, and transport to and from retinol stores in the liver (7). Thus, circulating retinol concentration is a better measurement of retinol status than self-reported intake.

    Several epidemiologic studies have examined circulating retinol concentrations and risk of cancer, with inconsistent results. For example, an inverse association between retinol and cancer has been reported at several sites such as oral (8), liver (9–11), prostate (12, 13), lung (14–17), and stomach (18), while other studies have reported no association between retinol and cancers of the cervix (19), colon (20), prostate (21), breast (22), and liver (23). Meanwhile, a positive association between retinol and prostate cancer has been reported by several groups (24–27). In some of these studies, the inconsistency in the findings might be due to small sample sizes (i.e., n < 100), which limit the statistical power to detect true associations (12, 23). Furthermore, differences in study design, screening prevalence, dietary and lifestyle factors, and laboratory methods for measuring retinol could also contribute to the differences in the findings (8, 18). Therefore, to comprehensively evaluate the role of retinol across cancer sites within the same cohort, we investigated the association between serum retinol and risk of cancer at multiple sites within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study.

    METHODS

    Study design and population

    The ATBC Study was a randomized, double blind, placebo-controlled study conducted between 1985–1993. The objective of the study was to determine the effect of supplemental α-tocopherol and β-carotene on the incidence of lung and other cancers. Men from southwestern Finland, aged 50–69 years, who smoked at least 5 cigarettes per day, were recruited for the study. Informed consent was obtained from the participants, and the institutional review boards of both US National Cancer Institute and the National Public Health Institute of Finland approved the study. A total of 29,133 participants were randomized to one of four groups: 1) α-tocopherol (50 mg/day), 2) β-carotene (20 mg/day), 3) both supplements, or 4) placebo (28). The participants took the supplements until death or until April 30, 1993, when the trial ended.

    At the first baseline visit, the participants completed detailed questionnaires about their medical, smoking, and dietary history, and their height and weight were measured by registered nurses. Overnight fasting blood samples were collected and stored at −70°C, protected from light.

    Serum retinol measurement

    Baseline serum samples from all participants were analyzed for α-tocopherol, β-carotene, retinol, and total and high-density lipoprotein cholesterol. Retinol was measured using reverse-phase high-performance liquid chromatography (29). All the assays were conducted at a central laboratory at the National Public Health Institute in Helsinki, Finland. Of 29,133 participants, 29 were excluded due to missing retinol values, leaving a total analytical cohort of 29,104 men.

    Case identification

    All cases were identified by the Finnish Cancer Registry, which has been shown to correctly identify and classify nearly all cancer cases for this cohort (30). For cases that were diagnosed before September 2001, medical records were reviewed by 1 or 2 oncologists to confirm diagnosis. For cases that were diagnosed after September 2001, information was obtained from the Finnish Cancer Registry and Register of Causes of Death (28). Included in this report are all of the following cancers, diagnosed through December 31, 2012 (n = 10,798): cancers of the biliary tract (International Classification of Diseases, Ninth Revision (ICD-9), code 156), bladder (ICD-9 code 188), brain/central nervous system (ICD-9 code 191), colorectum (ICD-9 codes 153 and 154, excluding cancers of appendix and anus), upper gastrointestinal tract (including esophageal squamous cell carcinoma (ICD-9 code 150), esophagogastric junctional adenocarcinoma (ICD-9 codes 150 and 151.0), and gastric noncardia adenocarcinoma (ICD-9 codes 151.1–151.9)), hematologic (ICD-9 codes 200–208), kidney (ICD-9 codes 189.0 and 189.1), larynx (ICD-9 code 161, including only squamous cancers), liver (including intrahepatic bile duct (ICD-9 code 155)), lung (ICD-9 code 162), melanoma (ICD-9 code 172), oropharynx (ICD-9 codes 140–149, including only squamous cancers), pancreas (ICD-9 code 157, excluding 157.4), and prostate (ICD-9 code 185). With the exception of two sites with small numbers (small bowel cancer, n = 36, and cancer of the pleura, n = 71), we included all site-specific cancers for which we received data from the Finnish Cancer Registry.

    Statistical analysis

    Cox proportional hazards regression was used to estimate the association between quintiles of baseline serum retinol concentration and overall as well as site-specific cancer incidence. We confirmed the proportional hazards assumption for all individual cancer sites examined (all P > 0.12) by including in the model a term for interaction between serum retinol and follow-up time and by evaluating its statistical significance using the Wald test. All models included age as a continuous variable. Risk factors known or hypothesized to be associated with different cancers were assessed as potential confounders by entering each factor in the age-adjusted model for overall cancer. The variables considered were: α-tocopherol treatment group; β-carotene treatment group; height; weight; body mass index; number of cigarettes smoked per day; years of smoking; marital status; education and training; physical activity; urban residence; intake of fruit, vegetables, red meat, dietary fat, cholesterol, alcohol, retinol, vitamin D, and calcium; and baseline concentrations of serum total and high-density lipoprotein cholesterol, α-tocopherol, and β-carotene. Although none of these variables met the definition of a confounder (i.e., their inclusion did not change the retinol point estimates by >10%), we selected the following covariables for inclusion in our multivariable models: α-tocopherol treatment group (yes/no), β-carotene treatment group (yes/no), alcohol consumption (up to vs. at least the median), age, body mass index, number of cigarettes smoked per day, years of smoking, serum α-tocopherol, serum β-carotene, and serum cholesterol (all continuous).

    To evaluate potential effect modification, models were stratified on the following variables: α-tocopherol treatment group, β-carotene treatment group, follow-up time (up to 10 years vs. at least 10 years), number of cigarettes smoked per day, number of years smoked regularly, body mass index, alcohol consumption, serum α-tocopherol, serum β-carotene, serum cholesterol, and age (all up to vs. at least the median). Stratified analyses were performed for overall cancer incidence, as well as for those cancers where a main association with serum retinol was observed (i.e., liver, prostate, lung). Statistical interaction was assessed using the likelihood ratio test by comparing models with and without an interaction term. All reported P values are 2 tailed, and α = 0.05 is considered to be the threshold for statistical significance for most analyses, with the exception of the exploratory interaction analyses where a Bonferroni correction was used to account for multiple testing (α = 0.00125 based on 44 tests). All analyses were performed using SAS, version 9.4 (SAS Institute, Inc., Cary, North Carolina).

    RESULTS

    Characteristics of the cohort according to baseline serum retinol concentration are shown in Table 1. Men with higher retinol status had higher average body mass index, serum total and high-density lipoprotein cholesterol, and serum α-tocopherol, and were more highly educated. Men with higher serum retinol also had higher dietary intakes of total vegetables, red meat, alcohol, and retinol, and were more likely to take calcium, vitamin A, vitamin D, and vitamin E supplements.

    Table 1

    Selected Baseline Characteristics According to Serum Retinol Quintiles in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Finland, 1985–2012

    CharacteristicQuintile of Baseline Serum Retinola
    1(n = 5,883)2(n = 5,806)3(n = 5,841)4(n = 5,792)5(n = 5,782)
    No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)
    α-tocopherol treatment group 2,946 49.9 2,904 50.1 2,930 50.3 2,876 49.6 2,891 50.1 
    β-carotene treatment group 2,955 50.1 2,896 49.9 2,897 49.7 2,925 50.4 2,875 49.9 
    Age, years 58 (51–65) 57 (51–65) 57 (51–65) 56 (51–64) 56 (51–64) 
    Height, cm 173 (164–180) 173 (166–181) 174 (166–189) 174 (166–182) 174 (166–182) 
    Weight, kg 75.6 (60.7–93.7) 77.7 (63.5–95.2) 78.4 (64.2–96.2) 79.1 (65.5–96.0) 80.4 (66.0–97.3) 
    Body mass indexb25.3 (20.8–30.6) 25.8 (21.6–31.1) 26 (21.9–31.2) 26.2 (22.2–31.2) 26.6 (31.5–22.5) 
    Education and training combined above eighth grade 3,690 62.7 3,684 63.4 3,852 65.9 3,913 67.5 4,084 70.6 
    Married 4,603 78.2 4,700 81 4,743 81.2 4,706 81.2 4,592 79.4 
    Urban residence 3,578 60.8 3,362 57.9 3,465 59.3 3,336 57.6 3,456 59.7 
    Physically active 3,307 56.2 3,392 58.4 3,455 59.2 3,459 59.7 3,310 57.3 
    No. of cigarettes smoked per day 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 
    No. of years smoked regularly 39 (28–47) 37 (27–45) 36 (25–45) 36 (25–45) 35 (25–45) 
    Serum biomarkers 
     Retinol, mg/L 438 (357–476) 517 (491–542) 577 (553–601) 642 (614–675) 755 (697–900) 
     Total cholesterol, mmol/L 5.8 (4.5–7.3) 6.04 (4.81–7.53) 6.17 (4.90–7.73) 6.3 (5.0–7.9) 6.5 (5.1–8.0) 
     HDL cholesterol, mmol/L 1.12 (0.83–1.56) 1.14 (0.84–1.59) 1.14 (0.85–1.6) 1.15 (1.63–0.85) 1.17 (0.85–1.71) 
     α-tocopherol, mg/L 10.6 (7.5–14.3) 11.1 (8.2–15.1) 11.6 (8.5–15.6) 11.9 (8.7–16.3) 12.4 (8.8–17.6) 
     β-carotene, μg/L 171 (70–385) 177 (76–386) 180 (79–395) 175 (72–390) 151 (59–357) 
    Daily dietary intake 
     Cholesterol, mg 529 (316–877) 535 (319–891) 539 (331–882) 546 (328–901) 542 (318–881) 
     Total fat, g 118 (76.5–176.1) 119 (77.2–178.8) 119 (78–177) 117 (77–175) 115 (73–173) 
     Retinol, mg 1,172 (519–2,634) 1,213 (545–2,748) 1,249 (542–2,746) 1,273 (571–2,809) 1,326 (552–2,872) 
     Vitamin D, mg 4.56 (2.08–9.33) 4.66 (2.21–9.21) 4.62 (2.18–9.20) 4.81 (2.25–9.41) 4.87 (2.33–9.52) 
     Vitamin E, mg 10.7 (6.3–20.2) 10.7 (6.4–19.7) 10.6 (6.4–19.2) 10.8 (6.4–19.5) 10.6 (6.3–18.9) 
     Calcium, mg 1,303 (717–2061) 1,338 (752–2,113) 1,334 (746–2,113) 1,347 (766–2,126) 1,325 (721–2,095) 
     Total energy, kcal 2,579 (2,123–3,664) 2,616 (1,820–3,666) 2,610 (1,847–3,657) 2,613 (1,854–3,676) 2,585 (2,142–3,659) 
     Alcohol, g 6.6 (0–34.9) 8.3 (0–35.7) 10.6 (0–40.6) 13.2 (0.4–46.4) 19.4 (1.3–56.3) 
     Total fruits, g 107 (25–250) 109 (27–250) 108 (25–252) 109 (27–253) 107 (23–258) 
     Total vegetables, g 87 (32.3–190) 91 (35–187) 95 (38–197) 99 (37–202) 98 (38–207) 
     Total red meat, g 129 (72–235) 132 (73–236) 132 (75–231) 134 (76–235) 133 (73.6–233) 
    Supplement use 
     Vitamin A 504 16.8 519 17.3 586 19.6 631 21.1 747 25.0 
     Vitamin D 316 16.1 335 17.0 385 19.5 433 22.0 499 25.3 
     Calcium 535 16.7 546 17.1 613 19.2 681 21.3 814 25.5 
     Vitamin E 472 16.1 524 17.8 582 19.8 630 21.4 725 24.7 
    CharacteristicQuintile of Baseline Serum Retinola
    1(n = 5,883)2(n = 5,806)3(n = 5,841)4(n = 5,792)5(n = 5,782)
    No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)
    α-tocopherol treatment group 2,946 49.9 2,904 50.1 2,930 50.3 2,876 49.6 2,891 50.1 
    β-carotene treatment group 2,955 50.1 2,896 49.9 2,897 49.7 2,925 50.4 2,875 49.9 
    Age, years 58 (51–65) 57 (51–65) 57 (51–65) 56 (51–64) 56 (51–64) 
    Height, cm 173 (164–180) 173 (166–181) 174 (166–189) 174 (166–182) 174 (166–182) 
    Weight, kg 75.6 (60.7–93.7) 77.7 (63.5–95.2) 78.4 (64.2–96.2) 79.1 (65.5–96.0) 80.4 (66.0–97.3) 
    Body mass indexb25.3 (20.8–30.6) 25.8 (21.6–31.1) 26 (21.9–31.2) 26.2 (22.2–31.2) 26.6 (31.5–22.5) 
    Education and training combined above eighth grade 3,690 62.7 3,684 63.4 3,852 65.9 3,913 67.5 4,084 70.6 
    Married 4,603 78.2 4,700 81 4,743 81.2 4,706 81.2 4,592 79.4 
    Urban residence 3,578 60.8 3,362 57.9 3,465 59.3 3,336 57.6 3,456 59.7 
    Physically active 3,307 56.2 3,392 58.4 3,455 59.2 3,459 59.7 3,310 57.3 
    No. of cigarettes smoked per day 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 
    No. of years smoked regularly 39 (28–47) 37 (27–45) 36 (25–45) 36 (25–45) 35 (25–45) 
    Serum biomarkers 
     Retinol, mg/L 438 (357–476) 517 (491–542) 577 (553–601) 642 (614–675) 755 (697–900) 
     Total cholesterol, mmol/L 5.8 (4.5–7.3) 6.04 (4.81–7.53) 6.17 (4.90–7.73) 6.3 (5.0–7.9) 6.5 (5.1–8.0) 
     HDL cholesterol, mmol/L 1.12 (0.83–1.56) 1.14 (0.84–1.59) 1.14 (0.85–1.6) 1.15 (1.63–0.85) 1.17 (0.85–1.71) 
     α-tocopherol, mg/L 10.6 (7.5–14.3) 11.1 (8.2–15.1) 11.6 (8.5–15.6) 11.9 (8.7–16.3) 12.4 (8.8–17.6) 
     β-carotene, μg/L 171 (70–385) 177 (76–386) 180 (79–395) 175 (72–390) 151 (59–357) 
    Daily dietary intake 
     Cholesterol, mg 529 (316–877) 535 (319–891) 539 (331–882) 546 (328–901) 542 (318–881) 
     Total fat, g 118 (76.5–176.1) 119 (77.2–178.8) 119 (78–177) 117 (77–175) 115 (73–173) 
     Retinol, mg 1,172 (519–2,634) 1,213 (545–2,748) 1,249 (542–2,746) 1,273 (571–2,809) 1,326 (552–2,872) 
     Vitamin D, mg 4.56 (2.08–9.33) 4.66 (2.21–9.21) 4.62 (2.18–9.20) 4.81 (2.25–9.41) 4.87 (2.33–9.52) 
     Vitamin E, mg 10.7 (6.3–20.2) 10.7 (6.4–19.7) 10.6 (6.4–19.2) 10.8 (6.4–19.5) 10.6 (6.3–18.9) 
     Calcium, mg 1,303 (717–2061) 1,338 (752–2,113) 1,334 (746–2,113) 1,347 (766–2,126) 1,325 (721–2,095) 
     Total energy, kcal 2,579 (2,123–3,664) 2,616 (1,820–3,666) 2,610 (1,847–3,657) 2,613 (1,854–3,676) 2,585 (2,142–3,659) 
     Alcohol, g 6.6 (0–34.9) 8.3 (0–35.7) 10.6 (0–40.6) 13.2 (0.4–46.4) 19.4 (1.3–56.3) 
     Total fruits, g 107 (25–250) 109 (27–250) 108 (25–252) 109 (27–253) 107 (23–258) 
     Total vegetables, g 87 (32.3–190) 91 (35–187) 95 (38–197) 99 (37–202) 98 (38–207) 
     Total red meat, g 129 (72–235) 132 (73–236) 132 (75–231) 134 (76–235) 133 (73.6–233) 
    Supplement use 
     Vitamin A 504 16.8 519 17.3 586 19.6 631 21.1 747 25.0 
     Vitamin D 316 16.1 335 17.0 385 19.5 433 22.0 499 25.3 
     Calcium 535 16.7 546 17.1 613 19.2 681 21.3 814 25.5 
     Vitamin E 472 16.1 524 17.8 582 19.8 630 21.4 725 24.7 

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    Table 1

    Selected Baseline Characteristics According to Serum Retinol Quintiles in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Finland, 1985–2012

    CharacteristicQuintile of Baseline Serum Retinola
    1(n = 5,883)2(n = 5,806)3(n = 5,841)4(n = 5,792)5(n = 5,782)
    No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)
    α-tocopherol treatment group 2,946 49.9 2,904 50.1 2,930 50.3 2,876 49.6 2,891 50.1 
    β-carotene treatment group 2,955 50.1 2,896 49.9 2,897 49.7 2,925 50.4 2,875 49.9 
    Age, years 58 (51–65) 57 (51–65) 57 (51–65) 56 (51–64) 56 (51–64) 
    Height, cm 173 (164–180) 173 (166–181) 174 (166–189) 174 (166–182) 174 (166–182) 
    Weight, kg 75.6 (60.7–93.7) 77.7 (63.5–95.2) 78.4 (64.2–96.2) 79.1 (65.5–96.0) 80.4 (66.0–97.3) 
    Body mass indexb25.3 (20.8–30.6) 25.8 (21.6–31.1) 26 (21.9–31.2) 26.2 (22.2–31.2) 26.6 (31.5–22.5) 
    Education and training combined above eighth grade 3,690 62.7 3,684 63.4 3,852 65.9 3,913 67.5 4,084 70.6 
    Married 4,603 78.2 4,700 81 4,743 81.2 4,706 81.2 4,592 79.4 
    Urban residence 3,578 60.8 3,362 57.9 3,465 59.3 3,336 57.6 3,456 59.7 
    Physically active 3,307 56.2 3,392 58.4 3,455 59.2 3,459 59.7 3,310 57.3 
    No. of cigarettes smoked per day 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 
    No. of years smoked regularly 39 (28–47) 37 (27–45) 36 (25–45) 36 (25–45) 35 (25–45) 
    Serum biomarkers 
     Retinol, mg/L 438 (357–476) 517 (491–542) 577 (553–601) 642 (614–675) 755 (697–900) 
     Total cholesterol, mmol/L 5.8 (4.5–7.3) 6.04 (4.81–7.53) 6.17 (4.90–7.73) 6.3 (5.0–7.9) 6.5 (5.1–8.0) 
     HDL cholesterol, mmol/L 1.12 (0.83–1.56) 1.14 (0.84–1.59) 1.14 (0.85–1.6) 1.15 (1.63–0.85) 1.17 (0.85–1.71) 
     α-tocopherol, mg/L 10.6 (7.5–14.3) 11.1 (8.2–15.1) 11.6 (8.5–15.6) 11.9 (8.7–16.3) 12.4 (8.8–17.6) 
     β-carotene, μg/L 171 (70–385) 177 (76–386) 180 (79–395) 175 (72–390) 151 (59–357) 
    Daily dietary intake 
     Cholesterol, mg 529 (316–877) 535 (319–891) 539 (331–882) 546 (328–901) 542 (318–881) 
     Total fat, g 118 (76.5–176.1) 119 (77.2–178.8) 119 (78–177) 117 (77–175) 115 (73–173) 
     Retinol, mg 1,172 (519–2,634) 1,213 (545–2,748) 1,249 (542–2,746) 1,273 (571–2,809) 1,326 (552–2,872) 
     Vitamin D, mg 4.56 (2.08–9.33) 4.66 (2.21–9.21) 4.62 (2.18–9.20) 4.81 (2.25–9.41) 4.87 (2.33–9.52) 
     Vitamin E, mg 10.7 (6.3–20.2) 10.7 (6.4–19.7) 10.6 (6.4–19.2) 10.8 (6.4–19.5) 10.6 (6.3–18.9) 
     Calcium, mg 1,303 (717–2061) 1,338 (752–2,113) 1,334 (746–2,113) 1,347 (766–2,126) 1,325 (721–2,095) 
     Total energy, kcal 2,579 (2,123–3,664) 2,616 (1,820–3,666) 2,610 (1,847–3,657) 2,613 (1,854–3,676) 2,585 (2,142–3,659) 
     Alcohol, g 6.6 (0–34.9) 8.3 (0–35.7) 10.6 (0–40.6) 13.2 (0.4–46.4) 19.4 (1.3–56.3) 
     Total fruits, g 107 (25–250) 109 (27–250) 108 (25–252) 109 (27–253) 107 (23–258) 
     Total vegetables, g 87 (32.3–190) 91 (35–187) 95 (38–197) 99 (37–202) 98 (38–207) 
     Total red meat, g 129 (72–235) 132 (73–236) 132 (75–231) 134 (76–235) 133 (73.6–233) 
    Supplement use 
     Vitamin A 504 16.8 519 17.3 586 19.6 631 21.1 747 25.0 
     Vitamin D 316 16.1 335 17.0 385 19.5 433 22.0 499 25.3 
     Calcium 535 16.7 546 17.1 613 19.2 681 21.3 814 25.5 
     Vitamin E 472 16.1 524 17.8 582 19.8 630 21.4 725 24.7 
    CharacteristicQuintile of Baseline Serum Retinola
    1(n = 5,883)2(n = 5,806)3(n = 5,841)4(n = 5,792)5(n = 5,782)
    No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)No.%Median (10th–90th Percentile)
    α-tocopherol treatment group 2,946 49.9 2,904 50.1 2,930 50.3 2,876 49.6 2,891 50.1 
    β-carotene treatment group 2,955 50.1 2,896 49.9 2,897 49.7 2,925 50.4 2,875 49.9 
    Age, years 58 (51–65) 57 (51–65) 57 (51–65) 56 (51–64) 56 (51–64) 
    Height, cm 173 (164–180) 173 (166–181) 174 (166–189) 174 (166–182) 174 (166–182) 
    Weight, kg 75.6 (60.7–93.7) 77.7 (63.5–95.2) 78.4 (64.2–96.2) 79.1 (65.5–96.0) 80.4 (66.0–97.3) 
    Body mass indexb25.3 (20.8–30.6) 25.8 (21.6–31.1) 26 (21.9–31.2) 26.2 (22.2–31.2) 26.6 (31.5–22.5) 
    Education and training combined above eighth grade 3,690 62.7 3,684 63.4 3,852 65.9 3,913 67.5 4,084 70.6 
    Married 4,603 78.2 4,700 81 4,743 81.2 4,706 81.2 4,592 79.4 
    Urban residence 3,578 60.8 3,362 57.9 3,465 59.3 3,336 57.6 3,456 59.7 
    Physically active 3,307 56.2 3,392 58.4 3,455 59.2 3,459 59.7 3,310 57.3 
    No. of cigarettes smoked per day 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 20 (10–30) 
    No. of years smoked regularly 39 (28–47) 37 (27–45) 36 (25–45) 36 (25–45) 35 (25–45) 
    Serum biomarkers 
     Retinol, mg/L 438 (357–476) 517 (491–542) 577 (553–601) 642 (614–675) 755 (697–900) 
     Total cholesterol, mmol/L 5.8 (4.5–7.3) 6.04 (4.81–7.53) 6.17 (4.90–7.73) 6.3 (5.0–7.9) 6.5 (5.1–8.0) 
     HDL cholesterol, mmol/L 1.12 (0.83–1.56) 1.14 (0.84–1.59) 1.14 (0.85–1.6) 1.15 (1.63–0.85) 1.17 (0.85–1.71) 
     α-tocopherol, mg/L 10.6 (7.5–14.3) 11.1 (8.2–15.1) 11.6 (8.5–15.6) 11.9 (8.7–16.3) 12.4 (8.8–17.6) 
     β-carotene, μg/L 171 (70–385) 177 (76–386) 180 (79–395) 175 (72–390) 151 (59–357) 
    Daily dietary intake 
     Cholesterol, mg 529 (316–877) 535 (319–891) 539 (331–882) 546 (328–901) 542 (318–881) 
     Total fat, g 118 (76.5–176.1) 119 (77.2–178.8) 119 (78–177) 117 (77–175) 115 (73–173) 
     Retinol, mg 1,172 (519–2,634) 1,213 (545–2,748) 1,249 (542–2,746) 1,273 (571–2,809) 1,326 (552–2,872) 
     Vitamin D, mg 4.56 (2.08–9.33) 4.66 (2.21–9.21) 4.62 (2.18–9.20) 4.81 (2.25–9.41) 4.87 (2.33–9.52) 
     Vitamin E, mg 10.7 (6.3–20.2) 10.7 (6.4–19.7) 10.6 (6.4–19.2) 10.8 (6.4–19.5) 10.6 (6.3–18.9) 
     Calcium, mg 1,303 (717–2061) 1,338 (752–2,113) 1,334 (746–2,113) 1,347 (766–2,126) 1,325 (721–2,095) 
     Total energy, kcal 2,579 (2,123–3,664) 2,616 (1,820–3,666) 2,610 (1,847–3,657) 2,613 (1,854–3,676) 2,585 (2,142–3,659) 
     Alcohol, g 6.6 (0–34.9) 8.3 (0–35.7) 10.6 (0–40.6) 13.2 (0.4–46.4) 19.4 (1.3–56.3) 
     Total fruits, g 107 (25–250) 109 (27–250) 108 (25–252) 109 (27–253) 107 (23–258) 
     Total vegetables, g 87 (32.3–190) 91 (35–187) 95 (38–197) 99 (37–202) 98 (38–207) 
     Total red meat, g 129 (72–235) 132 (73–236) 132 (75–231) 134 (76–235) 133 (73.6–233) 
    Supplement use 
     Vitamin A 504 16.8 519 17.3 586 19.6 631 21.1 747 25.0 
     Vitamin D 316 16.1 335 17.0 385 19.5 433 22.0 499 25.3 
     Calcium 535 16.7 546 17.1 613 19.2 681 21.3 814 25.5 
     Vitamin E 472 16.1 524 17.8 582 19.8 630 21.4 725 24.7 

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    Overall cancer

    After multivariable adjustment for several potential risk factors, serum retinol was not associated with overall cancer risk (for quintile 5 vs. 1, hazard ratio (HR) = 0.97, 95% confidence interval (CI) = 0.91, 1.03; P for trend = 0.43) (Table 2, Web Table 1). This finding was unchanged when cases diagnosed within 2 years of blood collection were excluded (for quintile 5 vs. 1, HR = 0.98, 95% CI: 0.92, 1.05; P for trend = 0.71) (Web Table 2). No statistically significant interaction was observed between serum retinol and any of the factors examined for overall cancer (Table 3).

    Table 2

    Association Between Baseline Serum Retinol and Overall and Site-Specific Cancer in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Finland, 1985–2012

    Cancer Site and ModelbNo. of CasesQuintile of Baseline Serum RetinolaP for Trend
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Overall Cancer 10,798 
     Model 1 0.94 0.88, 0.99 0.91 0.86, 0.97 0.92 0.86, 0.97 0.93 0.88, 0.99 0.39 
     Model 2 0.95 0.90, 1.01 0.93 0.88, 0.99 0.94 0.89, 1.01 0.97 0.91, 1.03 0.43 
    Biliary Tract 86 
     Model 1 1.01 0.50, 2.05 0.75 0.35, 1.60 1.59 0.83, 3.03 1.21 0.60, 2.41 0.28 
     Model 2 1.02 0.50, 2.07 0.76 0.35, 1.64 1.59 0.82, 3.06 1.19 0.59, 2.42 0.33 
    Bladder 789 
     Model 1 0.95 0.76, 1.18 0.96 0.76, 1.20 0.97 0.78, 1.22 1.01 0.81, 1.26 0.81 
     Model 2 0.94 0.75, 1.18 0.95 0.76, 1.18 0.96 0.77, 1.20 0.99 0.78, 1.24 0.97 
    Brain/CNS 78 
     Model 1 1.36 0.68, 2.69 1.00 0.48, 2.07 1.08 0.52, 2.21 0.90 0.42, 1.93 0.58 
     Model 2 0.72, 2.85 1.08 0.51, 2.26 1.18 0.57, 2.47 1.03 0.47, 2.25 0.85 
    Colorectum 878 
     Model 1 0.89 0.71, 1.11 1.05 0.85, 1.30 1.13 0.92, 1.40 1.03 0.83, 1.28 0.28 
     Model 2 0.89 0.71, 1.11 1.05 0.84, 1.30 1.12 0.91, 1.39 1.01 0.80, 1.26 0.40 
    ESCC 96 
     Model 1 0.89 0.46, 1.70 0.87 0.45, 1.66 0.88 0.46, 1.68 1.17 0.63, 2.16 0.55 
     Model 2 0.93 0.48, 1.77 0.92 0.48, 1.77 0.92 0.47, 1.78 1.16 0.61, 2.19 0.61 
    EGJA 151 
     Model 1 0.93 0.57, 1.51 0.89 0.55, 1.46 0.94 0.58, 1.53 0.71 0.41, 1.21 0.25 
     Model 2 0.93 0.57, 1.52 0.90 0.55, 1.47 0.94 0.57, 1.55 0.71 0.41, 1.24 0.28 
    GNCA 332 
     Model 1 1.06 0.75, 1.49 1.04 0.74, 1.47 1.16 0.83, 1.63 0.89 0.62, 1.28 0.69 
     Model 2 1.06 0.75, 1.50 1.05 0.74, 1.49 1.16 0.82, 1.63 0.89 0.61, 1.30 0.67 
    Hematologic 602 
     Model 1 1.11 0.86, 1.42 0.93 0.72, 1.21 1.00 0.78, 1.30 0.96 0.73, 1.24 0.55 
     Model 2 1.12 0.87, 1.43 0.93 0.72, 1.21 1.01 0.78, 1.31 0.97 0.74, 1.27 0.61 
    Kidney 413 
     Model 1 1.06 0.78, 1.44 0.96 0.70, 1.31 0.95 0.69, 1.31 1.15 0.85, 1.56 0.48 
     Model 2 1.06 0.77, 1.44 0.96 0.70, 1.32 0.96 0.69, 1.32 1.15 0.84, 1.58 0.48 
    Larynx 193 
     Model 1 1.12 0.70, 1.79 1.09 0.68, 1.75 1.36 0.87, 2.14 1.13 0.71, 1.82 0.44 
     Model 2 1.15 0.72, 1.85 1.15 0.71, 1.85 1.42 0.90, 2.25 1.12 0.69, 1.84 0.49 
    Liver 233 
     Model 1 0.62 0.42, 0.90 0.57 0.39, 0.84 0.35 0.23, 0.56 0.66 0.45, 0.96 0.006 
     Model 2 0.63 0.43, 0.92 0.59 0.40, 0.86 0.36 0.23, 0.57 0.62 0.42, 0.91 0.004 
     HCC, model 2 150 0.67 0.41, 1.09 0.78 0.49, 1.24 0.36 0.20, 0.65 0.65 0.40, 1.07 0.03 
    Lung 3,940 
     Model 1 0.90 0.81, 0.97 0.8 0.73, 0.88 0.75 0.68, 0.82 0.73 0.66, 0.81 <0.0001 
     Model 2 0.92 0.84, 1.01 0.85 0.77, 0.94 0.80 0.72, 0.88 0.80 0.72, 0.88 <0.0001 
    Melanoma 136 
     Model 1 1.24 0.70, 2.22 1.07 0.59, 1.94 1.30 0.73, 2.29 1.60 0.96, 2.88 0.06 
     Model 2 1.20 0.67, 2.16 1.02 0.56, 1.86 1.22 0.68, 2.18 1.52 0.87, 2.67 0.13 
    Oropharynx 239 
     Model 1 0.92 0.60, 1.40 1.12 0.75, 1.67 0.99 0.65, 1.49 1.07 0.71, 1.61 0.66 
     Model 2 0.98 0.64, 1.50 1.21 0.81, 1.82 1.07 0.70, 1.64 1.14 0.74, 1.74 0.49 
    Pancreas 454 
     Model 1 0.73 0.55, 0.98 0.69 0.52, 0.94 0.90 0.68, 1.18 0.89 0.67, 1.18 0.93 
     Model 2 0.72 0.54, 0.97 0.68 0.50, 0.92 0.87 0.66, 1.16 0.86 0.64, 1.15 0.77 
    Prostate 2,724 
     Model 1 1.08 0.95, 1.22 1.07 0.95, 1.21 1.12 0.99, 1.27 1.25 1.10, 1.41 0.0002 
     Model 2 1.09 0.96, 1.23 1.08 0.96, 1.23 1.14 1.00, 1.29 1.28 1.13, 1.45 <0.0001 
    Cancer Site and ModelbNo. of CasesQuintile of Baseline Serum RetinolaP for Trend
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Overall Cancer 10,798 
     Model 1 0.94 0.88, 0.99 0.91 0.86, 0.97 0.92 0.86, 0.97 0.93 0.88, 0.99 0.39 
     Model 2 0.95 0.90, 1.01 0.93 0.88, 0.99 0.94 0.89, 1.01 0.97 0.91, 1.03 0.43 
    Biliary Tract 86 
     Model 1 1.01 0.50, 2.05 0.75 0.35, 1.60 1.59 0.83, 3.03 1.21 0.60, 2.41 0.28 
     Model 2 1.02 0.50, 2.07 0.76 0.35, 1.64 1.59 0.82, 3.06 1.19 0.59, 2.42 0.33 
    Bladder 789 
     Model 1 0.95 0.76, 1.18 0.96 0.76, 1.20 0.97 0.78, 1.22 1.01 0.81, 1.26 0.81 
     Model 2 0.94 0.75, 1.18 0.95 0.76, 1.18 0.96 0.77, 1.20 0.99 0.78, 1.24 0.97 
    Brain/CNS 78 
     Model 1 1.36 0.68, 2.69 1.00 0.48, 2.07 1.08 0.52, 2.21 0.90 0.42, 1.93 0.58 
     Model 2 0.72, 2.85 1.08 0.51, 2.26 1.18 0.57, 2.47 1.03 0.47, 2.25 0.85 
    Colorectum 878 
     Model 1 0.89 0.71, 1.11 1.05 0.85, 1.30 1.13 0.92, 1.40 1.03 0.83, 1.28 0.28 
     Model 2 0.89 0.71, 1.11 1.05 0.84, 1.30 1.12 0.91, 1.39 1.01 0.80, 1.26 0.40 
    ESCC 96 
     Model 1 0.89 0.46, 1.70 0.87 0.45, 1.66 0.88 0.46, 1.68 1.17 0.63, 2.16 0.55 
     Model 2 0.93 0.48, 1.77 0.92 0.48, 1.77 0.92 0.47, 1.78 1.16 0.61, 2.19 0.61 
    EGJA 151 
     Model 1 0.93 0.57, 1.51 0.89 0.55, 1.46 0.94 0.58, 1.53 0.71 0.41, 1.21 0.25 
     Model 2 0.93 0.57, 1.52 0.90 0.55, 1.47 0.94 0.57, 1.55 0.71 0.41, 1.24 0.28 
    GNCA 332 
     Model 1 1.06 0.75, 1.49 1.04 0.74, 1.47 1.16 0.83, 1.63 0.89 0.62, 1.28 0.69 
     Model 2 1.06 0.75, 1.50 1.05 0.74, 1.49 1.16 0.82, 1.63 0.89 0.61, 1.30 0.67 
    Hematologic 602 
     Model 1 1.11 0.86, 1.42 0.93 0.72, 1.21 1.00 0.78, 1.30 0.96 0.73, 1.24 0.55 
     Model 2 1.12 0.87, 1.43 0.93 0.72, 1.21 1.01 0.78, 1.31 0.97 0.74, 1.27 0.61 
    Kidney 413 
     Model 1 1.06 0.78, 1.44 0.96 0.70, 1.31 0.95 0.69, 1.31 1.15 0.85, 1.56 0.48 
     Model 2 1.06 0.77, 1.44 0.96 0.70, 1.32 0.96 0.69, 1.32 1.15 0.84, 1.58 0.48 
    Larynx 193 
     Model 1 1.12 0.70, 1.79 1.09 0.68, 1.75 1.36 0.87, 2.14 1.13 0.71, 1.82 0.44 
     Model 2 1.15 0.72, 1.85 1.15 0.71, 1.85 1.42 0.90, 2.25 1.12 0.69, 1.84 0.49 
    Liver 233 
     Model 1 0.62 0.42, 0.90 0.57 0.39, 0.84 0.35 0.23, 0.56 0.66 0.45, 0.96 0.006 
     Model 2 0.63 0.43, 0.92 0.59 0.40, 0.86 0.36 0.23, 0.57 0.62 0.42, 0.91 0.004 
     HCC, model 2 150 0.67 0.41, 1.09 0.78 0.49, 1.24 0.36 0.20, 0.65 0.65 0.40, 1.07 0.03 
    Lung 3,940 
     Model 1 0.90 0.81, 0.97 0.8 0.73, 0.88 0.75 0.68, 0.82 0.73 0.66, 0.81 <0.0001 
     Model 2 0.92 0.84, 1.01 0.85 0.77, 0.94 0.80 0.72, 0.88 0.80 0.72, 0.88 <0.0001 
    Melanoma 136 
     Model 1 1.24 0.70, 2.22 1.07 0.59, 1.94 1.30 0.73, 2.29 1.60 0.96, 2.88 0.06 
     Model 2 1.20 0.67, 2.16 1.02 0.56, 1.86 1.22 0.68, 2.18 1.52 0.87, 2.67 0.13 
    Oropharynx 239 
     Model 1 0.92 0.60, 1.40 1.12 0.75, 1.67 0.99 0.65, 1.49 1.07 0.71, 1.61 0.66 
     Model 2 0.98 0.64, 1.50 1.21 0.81, 1.82 1.07 0.70, 1.64 1.14 0.74, 1.74 0.49 
    Pancreas 454 
     Model 1 0.73 0.55, 0.98 0.69 0.52, 0.94 0.90 0.68, 1.18 0.89 0.67, 1.18 0.93 
     Model 2 0.72 0.54, 0.97 0.68 0.50, 0.92 0.87 0.66, 1.16 0.86 0.64, 1.15 0.77 
    Prostate 2,724 
     Model 1 1.08 0.95, 1.22 1.07 0.95, 1.21 1.12 0.99, 1.27 1.25 1.10, 1.41 0.0002 
     Model 2 1.09 0.96, 1.23 1.08 0.96, 1.23 1.14 1.00, 1.29 1.28 1.13, 1.45 <0.0001 

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    Table 2

    Association Between Baseline Serum Retinol and Overall and Site-Specific Cancer in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Finland, 1985–2012

    Cancer Site and ModelbNo. of CasesQuintile of Baseline Serum RetinolaP for Trend
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Overall Cancer 10,798 
     Model 1 0.94 0.88, 0.99 0.91 0.86, 0.97 0.92 0.86, 0.97 0.93 0.88, 0.99 0.39 
     Model 2 0.95 0.90, 1.01 0.93 0.88, 0.99 0.94 0.89, 1.01 0.97 0.91, 1.03 0.43 
    Biliary Tract 86 
     Model 1 1.01 0.50, 2.05 0.75 0.35, 1.60 1.59 0.83, 3.03 1.21 0.60, 2.41 0.28 
     Model 2 1.02 0.50, 2.07 0.76 0.35, 1.64 1.59 0.82, 3.06 1.19 0.59, 2.42 0.33 
    Bladder 789 
     Model 1 0.95 0.76, 1.18 0.96 0.76, 1.20 0.97 0.78, 1.22 1.01 0.81, 1.26 0.81 
     Model 2 0.94 0.75, 1.18 0.95 0.76, 1.18 0.96 0.77, 1.20 0.99 0.78, 1.24 0.97 
    Brain/CNS 78 
     Model 1 1.36 0.68, 2.69 1.00 0.48, 2.07 1.08 0.52, 2.21 0.90 0.42, 1.93 0.58 
     Model 2 0.72, 2.85 1.08 0.51, 2.26 1.18 0.57, 2.47 1.03 0.47, 2.25 0.85 
    Colorectum 878 
     Model 1 0.89 0.71, 1.11 1.05 0.85, 1.30 1.13 0.92, 1.40 1.03 0.83, 1.28 0.28 
     Model 2 0.89 0.71, 1.11 1.05 0.84, 1.30 1.12 0.91, 1.39 1.01 0.80, 1.26 0.40 
    ESCC 96 
     Model 1 0.89 0.46, 1.70 0.87 0.45, 1.66 0.88 0.46, 1.68 1.17 0.63, 2.16 0.55 
     Model 2 0.93 0.48, 1.77 0.92 0.48, 1.77 0.92 0.47, 1.78 1.16 0.61, 2.19 0.61 
    EGJA 151 
     Model 1 0.93 0.57, 1.51 0.89 0.55, 1.46 0.94 0.58, 1.53 0.71 0.41, 1.21 0.25 
     Model 2 0.93 0.57, 1.52 0.90 0.55, 1.47 0.94 0.57, 1.55 0.71 0.41, 1.24 0.28 
    GNCA 332 
     Model 1 1.06 0.75, 1.49 1.04 0.74, 1.47 1.16 0.83, 1.63 0.89 0.62, 1.28 0.69 
     Model 2 1.06 0.75, 1.50 1.05 0.74, 1.49 1.16 0.82, 1.63 0.89 0.61, 1.30 0.67 
    Hematologic 602 
     Model 1 1.11 0.86, 1.42 0.93 0.72, 1.21 1.00 0.78, 1.30 0.96 0.73, 1.24 0.55 
     Model 2 1.12 0.87, 1.43 0.93 0.72, 1.21 1.01 0.78, 1.31 0.97 0.74, 1.27 0.61 
    Kidney 413 
     Model 1 1.06 0.78, 1.44 0.96 0.70, 1.31 0.95 0.69, 1.31 1.15 0.85, 1.56 0.48 
     Model 2 1.06 0.77, 1.44 0.96 0.70, 1.32 0.96 0.69, 1.32 1.15 0.84, 1.58 0.48 
    Larynx 193 
     Model 1 1.12 0.70, 1.79 1.09 0.68, 1.75 1.36 0.87, 2.14 1.13 0.71, 1.82 0.44 
     Model 2 1.15 0.72, 1.85 1.15 0.71, 1.85 1.42 0.90, 2.25 1.12 0.69, 1.84 0.49 
    Liver 233 
     Model 1 0.62 0.42, 0.90 0.57 0.39, 0.84 0.35 0.23, 0.56 0.66 0.45, 0.96 0.006 
     Model 2 0.63 0.43, 0.92 0.59 0.40, 0.86 0.36 0.23, 0.57 0.62 0.42, 0.91 0.004 
     HCC, model 2 150 0.67 0.41, 1.09 0.78 0.49, 1.24 0.36 0.20, 0.65 0.65 0.40, 1.07 0.03 
    Lung 3,940 
     Model 1 0.90 0.81, 0.97 0.8 0.73, 0.88 0.75 0.68, 0.82 0.73 0.66, 0.81 <0.0001 
     Model 2 0.92 0.84, 1.01 0.85 0.77, 0.94 0.80 0.72, 0.88 0.80 0.72, 0.88 <0.0001 
    Melanoma 136 
     Model 1 1.24 0.70, 2.22 1.07 0.59, 1.94 1.30 0.73, 2.29 1.60 0.96, 2.88 0.06 
     Model 2 1.20 0.67, 2.16 1.02 0.56, 1.86 1.22 0.68, 2.18 1.52 0.87, 2.67 0.13 
    Oropharynx 239 
     Model 1 0.92 0.60, 1.40 1.12 0.75, 1.67 0.99 0.65, 1.49 1.07 0.71, 1.61 0.66 
     Model 2 0.98 0.64, 1.50 1.21 0.81, 1.82 1.07 0.70, 1.64 1.14 0.74, 1.74 0.49 
    Pancreas 454 
     Model 1 0.73 0.55, 0.98 0.69 0.52, 0.94 0.90 0.68, 1.18 0.89 0.67, 1.18 0.93 
     Model 2 0.72 0.54, 0.97 0.68 0.50, 0.92 0.87 0.66, 1.16 0.86 0.64, 1.15 0.77 
    Prostate 2,724 
     Model 1 1.08 0.95, 1.22 1.07 0.95, 1.21 1.12 0.99, 1.27 1.25 1.10, 1.41 0.0002 
     Model 2 1.09 0.96, 1.23 1.08 0.96, 1.23 1.14 1.00, 1.29 1.28 1.13, 1.45 <0.0001 
    Cancer Site and ModelbNo. of CasesQuintile of Baseline Serum RetinolaP for Trend
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Overall Cancer 10,798 
     Model 1 0.94 0.88, 0.99 0.91 0.86, 0.97 0.92 0.86, 0.97 0.93 0.88, 0.99 0.39 
     Model 2 0.95 0.90, 1.01 0.93 0.88, 0.99 0.94 0.89, 1.01 0.97 0.91, 1.03 0.43 
    Biliary Tract 86 
     Model 1 1.01 0.50, 2.05 0.75 0.35, 1.60 1.59 0.83, 3.03 1.21 0.60, 2.41 0.28 
     Model 2 1.02 0.50, 2.07 0.76 0.35, 1.64 1.59 0.82, 3.06 1.19 0.59, 2.42 0.33 
    Bladder 789 
     Model 1 0.95 0.76, 1.18 0.96 0.76, 1.20 0.97 0.78, 1.22 1.01 0.81, 1.26 0.81 
     Model 2 0.94 0.75, 1.18 0.95 0.76, 1.18 0.96 0.77, 1.20 0.99 0.78, 1.24 0.97 
    Brain/CNS 78 
     Model 1 1.36 0.68, 2.69 1.00 0.48, 2.07 1.08 0.52, 2.21 0.90 0.42, 1.93 0.58 
     Model 2 0.72, 2.85 1.08 0.51, 2.26 1.18 0.57, 2.47 1.03 0.47, 2.25 0.85 
    Colorectum 878 
     Model 1 0.89 0.71, 1.11 1.05 0.85, 1.30 1.13 0.92, 1.40 1.03 0.83, 1.28 0.28 
     Model 2 0.89 0.71, 1.11 1.05 0.84, 1.30 1.12 0.91, 1.39 1.01 0.80, 1.26 0.40 
    ESCC 96 
     Model 1 0.89 0.46, 1.70 0.87 0.45, 1.66 0.88 0.46, 1.68 1.17 0.63, 2.16 0.55 
     Model 2 0.93 0.48, 1.77 0.92 0.48, 1.77 0.92 0.47, 1.78 1.16 0.61, 2.19 0.61 
    EGJA 151 
     Model 1 0.93 0.57, 1.51 0.89 0.55, 1.46 0.94 0.58, 1.53 0.71 0.41, 1.21 0.25 
     Model 2 0.93 0.57, 1.52 0.90 0.55, 1.47 0.94 0.57, 1.55 0.71 0.41, 1.24 0.28 
    GNCA 332 
     Model 1 1.06 0.75, 1.49 1.04 0.74, 1.47 1.16 0.83, 1.63 0.89 0.62, 1.28 0.69 
     Model 2 1.06 0.75, 1.50 1.05 0.74, 1.49 1.16 0.82, 1.63 0.89 0.61, 1.30 0.67 
    Hematologic 602 
     Model 1 1.11 0.86, 1.42 0.93 0.72, 1.21 1.00 0.78, 1.30 0.96 0.73, 1.24 0.55 
     Model 2 1.12 0.87, 1.43 0.93 0.72, 1.21 1.01 0.78, 1.31 0.97 0.74, 1.27 0.61 
    Kidney 413 
     Model 1 1.06 0.78, 1.44 0.96 0.70, 1.31 0.95 0.69, 1.31 1.15 0.85, 1.56 0.48 
     Model 2 1.06 0.77, 1.44 0.96 0.70, 1.32 0.96 0.69, 1.32 1.15 0.84, 1.58 0.48 
    Larynx 193 
     Model 1 1.12 0.70, 1.79 1.09 0.68, 1.75 1.36 0.87, 2.14 1.13 0.71, 1.82 0.44 
     Model 2 1.15 0.72, 1.85 1.15 0.71, 1.85 1.42 0.90, 2.25 1.12 0.69, 1.84 0.49 
    Liver 233 
     Model 1 0.62 0.42, 0.90 0.57 0.39, 0.84 0.35 0.23, 0.56 0.66 0.45, 0.96 0.006 
     Model 2 0.63 0.43, 0.92 0.59 0.40, 0.86 0.36 0.23, 0.57 0.62 0.42, 0.91 0.004 
     HCC, model 2 150 0.67 0.41, 1.09 0.78 0.49, 1.24 0.36 0.20, 0.65 0.65 0.40, 1.07 0.03 
    Lung 3,940 
     Model 1 0.90 0.81, 0.97 0.8 0.73, 0.88 0.75 0.68, 0.82 0.73 0.66, 0.81 <0.0001 
     Model 2 0.92 0.84, 1.01 0.85 0.77, 0.94 0.80 0.72, 0.88 0.80 0.72, 0.88 <0.0001 
    Melanoma 136 
     Model 1 1.24 0.70, 2.22 1.07 0.59, 1.94 1.30 0.73, 2.29 1.60 0.96, 2.88 0.06 
     Model 2 1.20 0.67, 2.16 1.02 0.56, 1.86 1.22 0.68, 2.18 1.52 0.87, 2.67 0.13 
    Oropharynx 239 
     Model 1 0.92 0.60, 1.40 1.12 0.75, 1.67 0.99 0.65, 1.49 1.07 0.71, 1.61 0.66 
     Model 2 0.98 0.64, 1.50 1.21 0.81, 1.82 1.07 0.70, 1.64 1.14 0.74, 1.74 0.49 
    Pancreas 454 
     Model 1 0.73 0.55, 0.98 0.69 0.52, 0.94 0.90 0.68, 1.18 0.89 0.67, 1.18 0.93 
     Model 2 0.72 0.54, 0.97 0.68 0.50, 0.92 0.87 0.66, 1.16 0.86 0.64, 1.15 0.77 
    Prostate 2,724 
     Model 1 1.08 0.95, 1.22 1.07 0.95, 1.21 1.12 0.99, 1.27 1.25 1.10, 1.41 0.0002 
     Model 2 1.09 0.96, 1.23 1.08 0.96, 1.23 1.14 1.00, 1.29 1.28 1.13, 1.45 <0.0001 

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    Table 3

    Association Between Baseline Serum Retinol and Overall Cancer, Stratified by Potential Effect Modifiers, in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Finland, 1985–2012

    SubgroupaNo. of CasesQuintile of Baseline Serum Retinolb,cP for Interaction
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Trial supplementation group 
     β-carotene 0.62 
     Yes 5,441 0.91 0.84, 0.99 0.92 0.85, 1.00 0.89 0.82, 0.97 0.93 0.86, 1.01 
     No 5,386 0.97 0.89, 1.06 0.92 0.84, 1.00 0.95 0.87, 1.04 0.94 0.86, 1.03 
     α-tocopherol 0.13 
     Yes 5,386 0.90 0.83, 0.98 0.92 0.85, 1.00 0.87 0.80, 0.94 0.93 0.85, 1.01 
     No 5,441 0.98 0.90, 1.07 0.91 0.84, 0.99 0.98 0.90, 1.06 0.94 0.86, 1.03 
    No. of cigarettes smoked    per day 0.36 
      <20 3,694 0.89 0.80, 0.98 0.92 0.83, 1.01 0.92 0.83, 1.01 0.96 0.86, 1.06 
      ≥20 7,133 0.98 0.91, 1.05 0.92 0.86, 0.99 0.92 0.86, 0.99 0.93 0.86, 1.00 
    No. of years smoked    regularly 0.08 
      <36 4,461 0.94 0.85, 1.04 0.97 0.88, 1.07 0.98 0.89, 1.08 1.02 0.93, 1.12 
     ≥36 6,366 0.95 0.88, 1.02 0.9 0.83, 0.97 0.9 0.83, 0.97 0.9 0.83, 0.97 
    Body mass indexd0.21 
      <26 5,649 1.00 0.92, 1.08 0.96 0.88, 1.03 0.98 0.90, 1.06 0.98 0.90, 1.07 
      ≥26 5,178 0.88 0.80, 0.96 0.87 0.80, 0.96 0.86 0.79, 0.94 0.89 0.81, 0.97 
    Alcohol consumption, g 0.32 
      <11 5,793 0.98 0.90, 1.05 0.92 0.85, 0.99 0.96 0.89, 1.04 0.95 0.87, 1.04 
      ≥11 5,034 0.88 0.80, 0.97 0.89 0.81, 0.97 0.84 0.77, 0.93 0.87 0.79, 0.95 
    Serum α-tocopherol, mg/L 0.88 
      <11.5 5,499 0.93 0.86, 1.00 0.92 0.85, 1.00 0.94 0.87, 1.02 0.94 0.86, 1.03 
      ≥11.5 5,328 0.98 0.89, 1.08 0.94 0.86, 1.03 0.94 0.86, 1.03 0.98 0.89, 1.07 
    Serum β-carotene, μg/L 0.15 
      <170 5,283 0.91 0.84, 0.99 0.85 0.78, 0.92 0.9 0.83, 0.98 0.9 0.83, 0.98 
      ≥170 5,544 0.97 0.90, 1.06 0.99 0.91, 1.07 0.94 0.86, 1.02 0.94 0.86, 1.03 
    Serum total cholesterol,    mmol/L 0.11 
      <6.14 5,449 0.89 0.82, 0.96 0.88 0.81, 0.95 0.91 0.85, 1.00 0.94 0.86, 1.02 
      ≥6.14 5,378 1.03 0.94, 1.13 0.98 0.90, 1.08 0.96 0.87, 1.05 0.97 0.89, 1.06 
    Age, years 0.39 
      <57 4,897 0.95 0.86, 1.04 0.93 0.85, 1.03 0.95 0.87, 1.05 0.99 0.91, 1.09 
      ≥57 5,930 0.94 0.87, 1.02 0.91 0.85, 0.99 0.9 0.83, 0.98 0.89 0.82, 0.97 
    Follow-up time, years 0.16 
      <10 4,001 0.97 0.88, 1.06 1.04 0.94, 1.14 1.03 0.93, 1.13 0.95 0.86, 1.04 
      ≥10 6,826 1.00 0.93, 1.08 0.95 0.88, 1.02 0.98 0.91, 1.06 0.99 0.91, 1.07 
    SubgroupaNo. of CasesQuintile of Baseline Serum Retinolb,cP for Interaction
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Trial supplementation group 
     β-carotene 0.62 
     Yes 5,441 0.91 0.84, 0.99 0.92 0.85, 1.00 0.89 0.82, 0.97 0.93 0.86, 1.01 
     No 5,386 0.97 0.89, 1.06 0.92 0.84, 1.00 0.95 0.87, 1.04 0.94 0.86, 1.03 
     α-tocopherol 0.13 
     Yes 5,386 0.90 0.83, 0.98 0.92 0.85, 1.00 0.87 0.80, 0.94 0.93 0.85, 1.01 
     No 5,441 0.98 0.90, 1.07 0.91 0.84, 0.99 0.98 0.90, 1.06 0.94 0.86, 1.03 
    No. of cigarettes smoked    per day 0.36 
      <20 3,694 0.89 0.80, 0.98 0.92 0.83, 1.01 0.92 0.83, 1.01 0.96 0.86, 1.06 
      ≥20 7,133 0.98 0.91, 1.05 0.92 0.86, 0.99 0.92 0.86, 0.99 0.93 0.86, 1.00 
    No. of years smoked    regularly 0.08 
      <36 4,461 0.94 0.85, 1.04 0.97 0.88, 1.07 0.98 0.89, 1.08 1.02 0.93, 1.12 
     ≥36 6,366 0.95 0.88, 1.02 0.9 0.83, 0.97 0.9 0.83, 0.97 0.9 0.83, 0.97 
    Body mass indexd0.21 
      <26 5,649 1.00 0.92, 1.08 0.96 0.88, 1.03 0.98 0.90, 1.06 0.98 0.90, 1.07 
      ≥26 5,178 0.88 0.80, 0.96 0.87 0.80, 0.96 0.86 0.79, 0.94 0.89 0.81, 0.97 
    Alcohol consumption, g 0.32 
      <11 5,793 0.98 0.90, 1.05 0.92 0.85, 0.99 0.96 0.89, 1.04 0.95 0.87, 1.04 
      ≥11 5,034 0.88 0.80, 0.97 0.89 0.81, 0.97 0.84 0.77, 0.93 0.87 0.79, 0.95 
    Serum α-tocopherol, mg/L 0.88 
      <11.5 5,499 0.93 0.86, 1.00 0.92 0.85, 1.00 0.94 0.87, 1.02 0.94 0.86, 1.03 
      ≥11.5 5,328 0.98 0.89, 1.08 0.94 0.86, 1.03 0.94 0.86, 1.03 0.98 0.89, 1.07 
    Serum β-carotene, μg/L 0.15 
      <170 5,283 0.91 0.84, 0.99 0.85 0.78, 0.92 0.9 0.83, 0.98 0.9 0.83, 0.98 
      ≥170 5,544 0.97 0.90, 1.06 0.99 0.91, 1.07 0.94 0.86, 1.02 0.94 0.86, 1.03 
    Serum total cholesterol,    mmol/L 0.11 
      <6.14 5,449 0.89 0.82, 0.96 0.88 0.81, 0.95 0.91 0.85, 1.00 0.94 0.86, 1.02 
      ≥6.14 5,378 1.03 0.94, 1.13 0.98 0.90, 1.08 0.96 0.87, 1.05 0.97 0.89, 1.06 
    Age, years 0.39 
      <57 4,897 0.95 0.86, 1.04 0.93 0.85, 1.03 0.95 0.87, 1.05 0.99 0.91, 1.09 
      ≥57 5,930 0.94 0.87, 1.02 0.91 0.85, 0.99 0.9 0.83, 0.98 0.89 0.82, 0.97 
    Follow-up time, years 0.16 
      <10 4,001 0.97 0.88, 1.06 1.04 0.94, 1.14 1.03 0.93, 1.13 0.95 0.86, 1.04 
      ≥10 6,826 1.00 0.93, 1.08 0.95 0.88, 1.02 0.98 0.91, 1.06 0.99 0.91, 1.07 

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    Table 3

    Association Between Baseline Serum Retinol and Overall Cancer, Stratified by Potential Effect Modifiers, in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Finland, 1985–2012

    SubgroupaNo. of CasesQuintile of Baseline Serum Retinolb,cP for Interaction
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Trial supplementation group 
     β-carotene 0.62 
     Yes 5,441 0.91 0.84, 0.99 0.92 0.85, 1.00 0.89 0.82, 0.97 0.93 0.86, 1.01 
     No 5,386 0.97 0.89, 1.06 0.92 0.84, 1.00 0.95 0.87, 1.04 0.94 0.86, 1.03 
     α-tocopherol 0.13 
     Yes 5,386 0.90 0.83, 0.98 0.92 0.85, 1.00 0.87 0.80, 0.94 0.93 0.85, 1.01 
     No 5,441 0.98 0.90, 1.07 0.91 0.84, 0.99 0.98 0.90, 1.06 0.94 0.86, 1.03 
    No. of cigarettes smoked    per day 0.36 
      <20 3,694 0.89 0.80, 0.98 0.92 0.83, 1.01 0.92 0.83, 1.01 0.96 0.86, 1.06 
      ≥20 7,133 0.98 0.91, 1.05 0.92 0.86, 0.99 0.92 0.86, 0.99 0.93 0.86, 1.00 
    No. of years smoked    regularly 0.08 
      <36 4,461 0.94 0.85, 1.04 0.97 0.88, 1.07 0.98 0.89, 1.08 1.02 0.93, 1.12 
     ≥36 6,366 0.95 0.88, 1.02 0.9 0.83, 0.97 0.9 0.83, 0.97 0.9 0.83, 0.97 
    Body mass indexd0.21 
      <26 5,649 1.00 0.92, 1.08 0.96 0.88, 1.03 0.98 0.90, 1.06 0.98 0.90, 1.07 
      ≥26 5,178 0.88 0.80, 0.96 0.87 0.80, 0.96 0.86 0.79, 0.94 0.89 0.81, 0.97 
    Alcohol consumption, g 0.32 
      <11 5,793 0.98 0.90, 1.05 0.92 0.85, 0.99 0.96 0.89, 1.04 0.95 0.87, 1.04 
      ≥11 5,034 0.88 0.80, 0.97 0.89 0.81, 0.97 0.84 0.77, 0.93 0.87 0.79, 0.95 
    Serum α-tocopherol, mg/L 0.88 
      <11.5 5,499 0.93 0.86, 1.00 0.92 0.85, 1.00 0.94 0.87, 1.02 0.94 0.86, 1.03 
      ≥11.5 5,328 0.98 0.89, 1.08 0.94 0.86, 1.03 0.94 0.86, 1.03 0.98 0.89, 1.07 
    Serum β-carotene, μg/L 0.15 
      <170 5,283 0.91 0.84, 0.99 0.85 0.78, 0.92 0.9 0.83, 0.98 0.9 0.83, 0.98 
      ≥170 5,544 0.97 0.90, 1.06 0.99 0.91, 1.07 0.94 0.86, 1.02 0.94 0.86, 1.03 
    Serum total cholesterol,    mmol/L 0.11 
      <6.14 5,449 0.89 0.82, 0.96 0.88 0.81, 0.95 0.91 0.85, 1.00 0.94 0.86, 1.02 
      ≥6.14 5,378 1.03 0.94, 1.13 0.98 0.90, 1.08 0.96 0.87, 1.05 0.97 0.89, 1.06 
    Age, years 0.39 
      <57 4,897 0.95 0.86, 1.04 0.93 0.85, 1.03 0.95 0.87, 1.05 0.99 0.91, 1.09 
      ≥57 5,930 0.94 0.87, 1.02 0.91 0.85, 0.99 0.9 0.83, 0.98 0.89 0.82, 0.97 
    Follow-up time, years 0.16 
      <10 4,001 0.97 0.88, 1.06 1.04 0.94, 1.14 1.03 0.93, 1.13 0.95 0.86, 1.04 
      ≥10 6,826 1.00 0.93, 1.08 0.95 0.88, 1.02 0.98 0.91, 1.06 0.99 0.91, 1.07 
    SubgroupaNo. of CasesQuintile of Baseline Serum Retinolb,cP for Interaction
    2345
    HR95% CIHR95% CIHR95% CIHR95% CI
    Trial supplementation group 
     β-carotene 0.62 
     Yes 5,441 0.91 0.84, 0.99 0.92 0.85, 1.00 0.89 0.82, 0.97 0.93 0.86, 1.01 
     No 5,386 0.97 0.89, 1.06 0.92 0.84, 1.00 0.95 0.87, 1.04 0.94 0.86, 1.03 
     α-tocopherol 0.13 
     Yes 5,386 0.90 0.83, 0.98 0.92 0.85, 1.00 0.87 0.80, 0.94 0.93 0.85, 1.01 
     No 5,441 0.98 0.90, 1.07 0.91 0.84, 0.99 0.98 0.90, 1.06 0.94 0.86, 1.03 
    No. of cigarettes smoked    per day 0.36 
      <20 3,694 0.89 0.80, 0.98 0.92 0.83, 1.01 0.92 0.83, 1.01 0.96 0.86, 1.06 
      ≥20 7,133 0.98 0.91, 1.05 0.92 
    Sours: https://academic.oup.com/aje/article/189/6/532/5587086

    Cancer retinoid

    Retinoids, retinoic acid receptors, and cancer

    Retinoids (i.e., vitamin A, all-trans retinoic acid, and related signaling molecules) induce the differentiation of various types of stem cells. Nuclear retinoic acid receptors mediate most but not all of the effects of retinoids. Retinoid signaling is often compromised early in carcinogenesis, which suggests that a reduction in retinoid signaling may be required for tumor development. Retinoids interact with other signaling pathways, including estrogen signaling in breast cancer. Retinoids are used to treat cancer, in part because of their ability to induce differentiation and arrest proliferation. Delivery of retinoids to patients is challenging because of the rapid metabolism of some retinoids and because epigenetic changes can render cells retinoid resistant. Successful cancer therapy with retinoids is likely to require combination therapy with drugs that regulate the epigenome, such as DNA methyltransferase and histone deacetylase inhibitors, as well as classical chemotherapeutic agents. Thus, retinoid research benefits both cancer prevention and cancer treatment.

    Sours: https://pubmed.ncbi.nlm.nih.gov/21073338/
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