*3.4. Power Loss Recovery Step of Metadata Log and Partial Parity*

The metadata log and partial parity retained in memory can be lost from power failure; however, it can be recovered by recovery process. Since the metadata changes after the snapshot are logged to the StripeMapLog block, we can recover metadata changes by scanning the MapLog blocks. For the paritis, almost all parity data are stored in flash memory as a status of parity for full stripe write; the actual portion to be recovered is just the partial parity of write data for the corresponding stripe before power loss.

The metadata log and parity recovery steps are as follows. This step is power loss recovery step, not error recovery step. At the first step, flash storage tries to find the latest RootInfo by scanning *Root* block. In the second step, it recovers the metadata snapshot that consists of StripeInfoList, StripeBlkInfo, BlkInfo, and page mapping table by retrieving StripeMap and PageMap blocks. In the third step, the metadata changes, that occur after the snapshot, were stored in the StripeMapLog blocks, so they are recovered by reading page by page and updating each information. The last step to be recovered is the metadata changes that were losted due to the power loss are recovered. Those were just maintained in-memory StripeMapLogInfo so were not flushed to StripeMapLog block. Metadata changes of the StripeMapLogInfo can be recovered by scanning the corresponding stripe group itself, and we can identify the stripe group number from last valid page in the StripeMapLog Block since we record the number of NextStripe group at last valid StripeMapLog. So, the mapping changes of the NextStripe group can be recovered by scanning the spare region of each page. If the page has valid logical address in the spare region, which means the data are stored safely so that its metadata should be updated. If not, the data are not stored safely and the pages are abandoned. For the scanning of each stripe page, if all the pages of the stripe have valid logical address, which means all the data for the stripe are stored safely, the corresponding parity is also safe. If some pages do not have valid logical addresses, which means not all the data in this stripe are valid, then there is no valid parity for this stripe. For this stripe, each valid page is retrieved and parity is calculated with those. It is the partial parity that is lost by power failure. Therefore, we recovered partial parity for this stripe.

#### **4. Evaluation**

In order to prove the validity of the proposed RAID scattering and adaptive ECC, we implemented the proposed schemes inside FlashSim [34,35] that is widely used in the storage system evaluation. This open source simulator includes the simulated NAND flash controller and NAND flash memories that can be set for page size, block size, etc. The simulator also implements well-known FTLs and NAND flash operations, including read, write, and erase, as well as garbage collection. We have added the RAID parity management, compression-based adaptive ECC, and RAID-scattering schemes in this simulator. The simulation configuration set for performance evaluation is as follows. The page-level FTL was used for an FTL mapping management scheme, in which 8 KB was used for page size, and a 1 KB sub-page was used. The compression module uses the zlib [36] compression module. The BCH ECC model is used with 1024BCH16, which is an ECC module that can correct 16 bits per 1024 bytes. That is, 1024BCH16 is applied for the 1 KB sub-page. For the bit error generation and simulation, the BER is sampled and an error is injected according to its P/E cycles. The samples from BER versus P/E cycle data are taken from a previous study on NAND flash devices [24]. The simulated BER sample is depicted in Figure 2.

#### *4.1. Analysis of Adaptive ECC and RAID Scattering*

To evaluate the performance of the proposed system in different user environments, various IO traces, such as filesystem workloads, database workloads, web server access data, and media streaming IO, are collected by developers reflecting the real human behavior. The IO traces are collected and categorized as F-1, F-2, F-3, F-4, and F-5 according to the application's characteristics. Among those IO traces, F-1 and F-2 are text-dominant file system and database traces, F-3 and F-4 are media-dominant streaming application traces, and F-5 is a mix of IO traces.

We have measured the impact of compression for various IO traces. We applied page-based data compression using the zlib [36] compression module and measured the compression ratio for each page. The compression ratio distribution for IO traces from F-1 to F-5 is shown in Figure 7. As shown in the figure, compression ratios are distributed differently among all IO traces. For general filesystems and databases, large chunks of data are compressed to less than 50% of the original data, which means much of data for general filesystems and databases are highly compressed. The portion of text-like data in filesystems and databases is large, and these types of data are generally compressed at a high compression rate. On the contrary, for web servers or media streaming applications, not much data are compressed, as depicted in F-3 and F-4. Media-like data, such as videos, images, and audio, are dominant on web servers, which are inherently compressed data, and hence, the compression rate is low, which results in low compression rate.

**Figure 7.** Distribution for compressions for each dataset.

Based on the distribution for the data-compression rate of IO traces, we have evaluated the adaptive ECC scheme and the RAID-scattering scheme. For a fair evaluation, we have simulated IO operations over 10 million times and checked errors for each trace, as bit errors occur rarely. We measured the uncorrectable bit error rate (UBER) [1] for RBER as a performance metric by increasing the P/E cycle from 0 to 5000 for each experiment of each configuration. For UBER a page cannot be recovered by using the error-recovery scheme.

At first, the adaptive ECC scheme is evaluated to show its own performance contribution to error recovery. This is achieved by comparing with the original ECC scheme, that is, fixed-sized ECC encoding. The results of the error-recovery ability for adaptive ECC are plotted in Figure 8. The *x*-axis represents P/E cycles, and the *y*-axis represents UBER on a logarithmic scale. Thus, the result error rate after ECC decoding is presented as UBER. As per the results, the lower the UBER, the better the error-correction capability. As shown in Figure 8, the adaptive ECC scheme results in better UBER over legacy ECC for all IO traces, which means that the adaptive ECC scheme shows a better performance than the fixed ECC scheme in the correction of bit errors. The reason of this increase is the reduction in valid data from compression. From the figure, we can also identify that the performance gap increases for increasing P/E cycles. It means that more errors can be covered by adaptive ECC as more bit errors occur. Among IO traces, F-1 and F-2 result much better UBER than other traces. Because F-1 and F-2 achieve higher compression rates than others, these give smaller source sizes for ECC decoding. On the contrary, F-3 and F-4 show little performance improvement in comparison with that of the original ECC owing to inefficient compression.

**Figure 8.** Adaptive ECC scheme for IO traces from F-1 to F-5 in comparison with legacy.

Next, the RAID-scattering scheme is compared with the original RAID scheme having the RAID5 parity model. For this evaluation we have simulated the original RAID and RAID scattering with varying stripe sizes of four, eight, and 16. For all RAID and RAID-scattering configurations, UBER is evaluated as the P/E cycle increases from 0 to 5000. The experimental results are plotted in Figure 9. In all the figures, in accordance with the P/E cycle, RAID-Stripe4, RAID-Stripe8, and RAID-Stripe16 represent UBER for the original RAID configuration with stripe sizes four, eight, and 16, respectively, while Scatter4, Scatter8, and Scatter16 represent UBER for the RAID-scattering scheme with stripe sizes four, eight, and 16, respectively. We have also plotted Legacy UBER to compare results with the RAID-scattering scheme, as well as the RAID system. The Legacy shows the original uncorrected page error rate of the original fixed ECC scheme.

From Figure 9a,b,e, we identify that RAID scattering gives UBER enhancement over the legacy RAID system for F-1, F-2, and F-5. This is due to the high compression ratio of these traces. In the RAID system, the error-containing page can be recovered using the parity page and other data pages within the corresponding stripe. If more than one of those pages also has an uncorrected page error, the error-containing page fails to be recovered. As data are compressed and scattered within a stripe in the RAID-scattering system, there is less probability for overlap between error-containing pages than in the original RAID system. This results in less UBER for RAID-scattering systems for traces F-1, F-2, and F-5. For F-1 and F-2, we identify that RAID scattering with all stripe sizes give less UBER than legacy RAIDs with even smallest stripe size, that is, four. This means that even if the stripe size is large

and the parity covers a wider range, the error-correction capability is better than of the original RAID with a smaller stripe size. However, for IO traces with lower compression rates such as F-3 and F-4, as shown in Figure 9c,d, there is no big difference between UBER of the RAID-scattering scheme and UBER of the existing RAID scheme. As compression is less effective for F-3 and F-4, the number of pages to be recovered in the corresponding stripe has an error rate that does not differ much from that of the original RAID scheme. Even so, the figure shows that there is some performance improvement for each P/E cycle.

**Figure 9.** Experimental results for RAID scattering in comparison with original RAID with various stripe sizes. In accordance with P/E cycle, UBERs of RAID Scattering and original RAID are estimated along with stripe sizes four, eight, and 16.(**a**) RAID Scattering for F-1, (**b**) RAID Scattering for F-2, (**c**) RAID Scattering for F-3, (**d**) RAID Scattering for F-4, (**e**) RAID Scattering for F-5.

Finally, the RAID-scattering scheme with the adaptive ECC is compared with the legacy RAID system. In this experiment, each IO trace is modeled as compressed, encoded with adaptive ECC, and stored as scattering within the stripe in the RAID system. Like for the previous evaluation, we have simulated those by varying stripe sizes with four, eight, and 16. For all RAID and RAID scattering with adaptive ECC configurations, UBER is evaluated as the P/E cycle increases from 0 to 5000. The results for traces from F-1 to F-5 are plotted from Figure 10a–e. Similar to the results of the RAID

scattering only scheme, all figures show plots for UBER for RAID-Stripe4, RAID-Stripe8, RAID-Stripe16, Adp.ECC-Scatter4, Adp.ECC-Scatter8, and Adp.ECC-Scatter16 configurations in accordance with the P/E cycle. From the figures, we identify that RAID scattering with adaptive ECC shows a lower UBER than that of legacy RAID, as well as RAID-scattering scheme. For all cases, it definitely results in less UBER than legacy RAID systems, regardless of the compression rate of the IO data. Another noticeable point is that the UBER for RAID scattering with adaptive ECC is less than that of the legacy RAID even though the stripe size is larger, which implies that the RAID-scattering scheme can enlarge the stripe size for the RAID system configuration with a higher reliability. In other words, by reducing parity overhead, RAID scattering can reduce write overhead at the same reliability level as it has less parity portion.

(**e**)Adp. ECC and Scattering for F-5

**Figure 10.** Experimental results for RAID scattering added by adaptive ECC in comparison with the original RAID with various stripe sizes four, eight, and 16. In accordance with P/E cycle, RAID-Stripe4, RAID-Stripe8, RAID-Stripe16, Adp. ECC-Scatter4, Adp. ECC-Scatter8, Ada. ECC-Scatter16 show UBER with stripe sizes four, eight, and 16, respectively. (**a**) Adp. ECC and Scattering for F-1, (**b**) Adp. ECC and Scattering for F-2, (**c**) Adp. ECC and Scattering for F-3, (**d**) Adp. ECC and Scattering for F-4, (**e**) Adp. ECC and Scattering for F-5.

To see how much pages can be skipped during the recovery stage in RAID scattering scheme, we have measured the number of pages to be skipped from the recovery operations for the error-occurred page. It was also done with the RAID configuration as stripe size four, eight and 16, respectively, and for each configuration, the five IO traces were performed. Figure 11 shows the ratio of pages skipped during each restoring error-occurred page in the RAID scattering scheme. In the figure, five IO traces are depicted as a group to the same stripe size level, and thus the *x*-axis represents groups of five IO traces according to stripe size level, and the *y*-axis represents the skip ratio. Since the skip ratio represents the ratio of pages that need not be read during recovery; the higher the skip ratio is, the better the recovery rate is as well as the shorter the recovery time is. From the results, it can be inferred that since the compression ratio is high as in the traces F-1 and F-2, the skip ratio is high, which results in reduced UBER. This is an effect of the RAID scattering scheme. On the other hand, the skip ratio of F-3 is low; there is little effect on RAID scattering, which results in little enhancement for UBER.

**Figure 11.** The skip ratio of pages when restoring error-occurred page in the stripe in the RAID scattering scheme.

#### *4.2. Analysis of Metadata and Parity Overhead*

The adaptive ECC and RAID-scattering schemes provide an effective error management method that can be achieved by reducing the size of the valid data regions on a page. However, additional overhead is required to support this operation. There is an overhead in storing and managing the size of the compressed data stored in each page. Specifically, a space for recording the compressed data size of the corresponding page is required for each page entry of the FTL mapping table. It takes about 14 bits or 3 bytes per entry. The necessary metadata size can estimated according to page size, block size, and capacity of flash device. For 128 GB flash device, whose page size and block size is 8 KB and 1 MB, respectively, the amount of estimated metadata blocks is about 119–122 blocks which includes two root blocks, four StripeMap blocks, 112 PageMap blocks and 1–4 MapLog blocks. The size is about 120 MB. If the capacity of flash device is double, the amount of estimated metadata blocks is almost double of previous one. For 512 GB flash device, whose page size and block size 8 KB and 3 MB, respectively, the amount of metadata blocks is about 155–158 blocks including two root, two StripeMap, 150 PageMap, and 1–4 MapLog blocks, and whose size is around 237 MB. From the estimation we identify that it is the PageMap block that occupies the largest weight in metadata, and it increases as the number of pages increases. Since we use about 7 bytes per entry of the pagemap to manage the physical address of compressed data, it is considerably larger than the PageMap which does not manage data compression. On the other hand, the additional metadata blocks for stripe management consumes less than 10 blocks for hundreds of GB of flash devices, which is not much overhead.

To see the metadata overhead in the aspect of NAND flash IO, we analyzed the overhead of the metadata logging IO operation. The overhead of the metadata log management is mainly composed of two parts. The first is page write operations that write StripeMapLogInfo, which is a log of metadata changes for the corresponding stripe group. The StripeMaplogInfo is written to a page in the StripeMapLog block whenever the page's current stripe group is used for data write. The other part involves the flushing of whole metadata snapshot, which occurs when all the pages

of StripeMapLog block are exhaused. The whole metadata snapshot includes PageMap, StripeMap, and RootInfo. To evaluate the metadata log overhead, we have generated random write operations to the simulator and measured the number of written pages for metadta logs, which include logging of StripeMapLogInfo and the flusing of the entire metadata. Among the metadata log overhead, the duration of writing metadata snapshot is dependent of the number of StripeMapLog blocks. If we have several StripeMapLog blocks, the duration of flushing metadata snapshot is longer since more StripeMapLogInfo can be written to the StripeMapLob blocks. Therefore, the experiment was conducted by changing the number of StripeMapLog blocks from one to four.

Figure 12 shows the results of metadata log overhead for the implemented metadata log system for RAID5 architecture with stripe size four, eight, and 16, respectively. As shown in the figure, metadata log overhead is almost 10 percent if the RAID5 configuration is set with four stripe size and 1 StripeMapLog block. However, the overhead decreases as the stripe size increases and the number of StripeMapLog block increases. As the stripe size increases, the number of pages included in the one stripe group increases, and thus the frequency of flushing the StripeMapLogInfo log increases. In addition, if the number of StripeMapLog blocks is increased, the number of flushing of whole metadata snapshots is reduced. From the results, it can be seen that when the number of StripeMapLog blocks is increased by four or more, the overall metadata log overhead can be reduced to 5% even if the capacity of flash device is hundreds of GB.

**Figure 12.** The results of metadata log overhead for the metadata log system for RAID5 architecture with stripe size four, eight, and 16, respectively.

Next, to analyze the parity overhead, we have measured the number of parity writes for random write requests while varying average request size from 4 KB to 128 KB. The experiments are done with two RAID5 configurations, that is, full stripe parity scheme and conventional RAID5 scheme. The full stripe parity scheme implemented in this paper was compared with the conventional RAID5 scheme, in which parities of RAID5 are updated whenever a write request occurs for the corresponding stripe. Those configurations are also set to four, eight, 16 stripe size, respectively.

The result of the parity overhead according to the average random request size is depicted in Figure 13, for each stripe size four, eight, 16, respectively. In the figure, the *x*-axis represents the average request size and the *y*-axis represents the number of parity writes per request. The number of parity writes per requests means the number of parities written for each request, and therefore, the lower the value, the smaller the parity overhead. Two RAID configurations, STLog and RAID5, are depicted, where STLog stands for the full stripe parity scheme and RAID5 for the conventional RAID5 scheme. As shown in the Figure 13a, if the stripe size is four, there is little difference between STLog and RAID5. There is lower parity overhead for small requests, and parity overhead becomes similar as the request size increases. However, if the stripe size is bigger, i.e., eight or 16, the parity overhead is largely reduced with the STLog parity management scheme as shown in Figure 13b,c. It is due to the partial parity buffering of the full stripe parity scheme. Particularly, when the request size is smaller than the stripe size, the partial parity buffering applied in STLog is more effective since parity updates are frequently performed in the conventional RAID5 scheme. If the request size

exceeds the stripe size, parity for full stripe write is created as it is, so STLog is no different from conventional RAID5.

**Figure 13.** Result of the number of parity writes per request according to the change in the average request size, for each stripe size four, eight, 16, respectively.

#### *4.3. Discussion about Compression Module*

The adaptive ECC and RAID scattering is applied based on lossless data compression, and thus the overhead of the data compression module is a performance-deteriorating factor for NAND flash memory devices. Although the software zlib module is used in our system for data compression, the compression module requires a lot of CPU computation, and hence it is not suitable for practical use in software implementations. However, recent compression hardware [37] or accelerator [38] further increases the practical possibilities of lossless data compression. In particular, [37] describes that it supports gzip/zlib/deflate data compression with approximately 100 Gbps bandwidth. If such a compression module is included in the NAND flash controller, it is expected to be able to perform data compression with little effect to the performance degradation of other modules, although it will increase the cost. In this paper, we could not analyze the performance degradation effect by the compression module, but in the future, there is a need to analyze the performance of the overall system including the hardware compression module. This is our further work.

### **5. Conclusions**

The density of flash memory devices has increased by moving to smaller geometries and storing more bits per cell; however, this generates a lot of data errors. The typical approach to check these increasing errors is the use of well-known ECCs; however, the current state of the ECC scheme it not on the same level with the evolution of errors of the current state of flash memory. The RAID technology can be employed for flash storage devices to enhance their reliability. However, RAID has inherent parity update overhead.

In this paper, we propose the enhancement for ECC ability for inside the page and RAID parity management for outside the page, with the help of lossless data compression. The compressed data are encoded with adaptively reduced source length, which increases the recovery ability of the ECC module. The adaptive ECC method can reduce the size of a source length in proportion to the compression ratio. As this reduces the source length to be encoded, the effect of lowering the code rate increases the error-correction rate. For the perspective of outside the page, the compressed data are placed at a specific position in the page. As the unused area in the page generated by compression can be used as a non-overlapping area in RAID scattering, it is effective during the RAID recovery stage. The unused area is spread out within the parity cluster, which reduces the error coverage range due to the possible skipping pages that are not overlapping with the error-occurred page. The experimental results show that adaptive ECC can enhance the ability of error recovery. Furthermore, the RAID-scattering scheme achieves a high reliability at the same RAID stripe level. As the next step of this study, we will apply compression, ECC, and RAID scattering modules as hardware modules to evaluate the actual overhead and performance impact. Therefore, a more rigorous comparison of the performance of the propose

scheme versus others could be an important task to improve the completeness of the proposed scheme. Consequently, we set the more rigorous performance evaluations as our further work.

**Author Contributions:** Conceptualization, S.-H.L. and K.-W.P.; methodology, S.-H.L. and K.-W.P.; software, S.-H.L.; validation, S.-H.L.; formal analysis, S.-H.L. and K.-W.P.; investigation, S.-H.L. and K.-W.P.; resources, S.-H.L. and K.-W.P.; data curation, S.-H.L. and K.-W.P.; writing–original draft preparation, S.-H.L. and K.-W.P.; writing–review and editing, S.-H.L. and K.-W.P.; visualization, S.-H.L. and K.-W.P.; supervision, S.-H.L. and K.-W.P.; project administration, S.-H.L. and K.-W.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (NRF-2019R1F1A1057503). This work was supported by Hankuk University of Foreign Studies Research Fund. Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (NRF-2020R1A2C4002737).

**Conflicts of Interest:** The authors declare no conflict of interest.


**3. Cancer stemness**

600 nt) single-stranded overhang at its 3' end (the G-overhang) which folds back and base pairs with the C-rich strand, forming a T-loop [1]. In humans, hexameric telomere repeats act as binding sites for various telomere-binding proteins collectively termed the shelterin complex, a dynamic ensemble of interactions that allows the cell to distinguish between natural chromosome ends and DNA double-strand breaks, preventing the cell's DNA damage response (DDR) from improper activation [2]. Telomeres undergo progressive shortening with each cell division as a result of incomplete lagging strand synthesis, less widely known endprocessing events, and oxidative damage [3, 4]. This so-called telomere erosion operates as a kind of mitotic clock that determines ageing of the whole organism and suppresses malignant transformation of its constituent cells. The biological function of telomeres is heavily regulated and relies on both a minimal length of telomeric DNA and the proper functioning of the associated shelterin complex. A unique enzyme termed telomerase assists in replicating linear chromosomes through *de novo* synthesis of telomeric repeats, thereby counteracting the progressive telomere erosion that would otherwise occur in its partial or complete absence. In addition to its role in telomere length homeostasis, telomerase also performs telomere lengthindependent functions such as modulation of gene expression. In a pathological context, telomerase's new talents are intimately related to tumour development and progression to metastatic disease. This chapter summarises the newly discovered extracurricular activities of telomerase and describe how these are involved in regulating cancer stemness, the stem-like

Telomerase is a conserved RNA-dependent DNA polymerase canonically responsible for the maintenance of telomere length above a critical threshold. Human telomerase is primarily localised in the nucleus, as deducible from its role in telomere biology, but it can also be found in other cellular compartments such as the cytosol and mitochondria [5]. Telomerase is ribonucleoprotein in nature and consists minimally and essentially of a protein catalytic subunit (telomerase reverse transcriptase, TERT) and a large RNA subunit (telomerase RNA, TER). Active human telomerase has a bilobal architecture where one TERT subunit and one TER subunit participate in the formation of each lobe and a hinge region connects the two lobes [6]. This conformationally flexible, dimeric structure of the human enzyme undoubtedly has profound functional implications with respect to the catalytic cycle. Firstly, during the synthesis of telomeric DNA by telomerase, the 3′ end of the G-overhang is positioned in the active site of TERT and aligned by base pairing with the 3' end of the RNA template in TER. Secondly, TERT catalyses the addition of deoxyribonucleotides to the chromosome substrate through reverse transcribing TER into hexameric telomere repeats until the 5' end of the RNA template is reached. Lastly, telomerase translocates and realigns with the newly synthesised 3' end of the chromosome substrate to restart the catalytic cycle [7, 8]. In spite of the fact that TERT and TER are the two subunits that provide the catalytic core of telomerase, there are several other molecules that associate with telomerase and are involved in its biogenesis, trafficking, recruitment, and activation. Some of the most well-known telomerase-associated

component of human tumours.

188 Telomere - A Complex End of a Chromosome

**2. Telomerase and the cancer connection**

Cancer cells within a single tumour often exist in distinct phenotypic states which differ in functional attributes. This so-called intratumoural heterogeneity originates from a myriad of cell types recruited to the tumour as well as from genetic, epigenetic and metabolic differences amongst the cancer cells themselves and may result in variable or unpredictable responses to treatment [23]. Postulated to be the driving force behind tumour maintenance, hypermalignant stem-like cells called cancer stem cells (CSCs) represent a unique dimension of intratumoural heterogeneity. This often-small subpopulation of cancer cells is thought to play pivotal roles in tumour initiation and progression, spreading, therapy resistance, and recurrence, all of which lead to poor prognosis.

#### **3.1. Definitions and measurements**

CSCs have critical implications for nearly, if not quite, all types of cancers, including leukae‐ mias [24–26], lymphomas [27], melanomas [28, 29], sarcomas [30], and various carcinomas such as brain [31], skin [32], head and neck [33], lung [34, 35], liver [36], gastric [37], colorectal [38, 39], bladder [40], pancreatic [41, 42], prostate [43], breast [44] and ovarian [45] cancers. The functions assumed by CSCs in human cancers are diverse, ranging from sustaining tumour growth and dissemination to treatment failure and tumour relapse. The three clear-cut features that contribute to the aforementioned functions of CSCs, aka cancer stemness traits, are: (i) their unrestricted ability to self-renew; (ii) their aberrant ability to differentiate into mixed populations of tumour cells; and (iii) their high ability to transition from a proliferative to a quiescent state. In spite of the fact that these operational characteristics are, to a large extent, shared by both CSCs and physiological stem cells, CSCs are the ones that are known to be related to several malignant phenotypes, including induction of invasion and metastasis and resistance to apoptosis. In addition, CSCs are distinguished from bulk tumour cells by their capacity to form nonadherent spheres when cultured in stem cell media, their propensity to found fresh tumours when transplanted into severe combined immunodeficient mice and their expression of a selected repertoire of stem cell-surface markers [46]. Therefore, it is important here to realise that cancer stemness, no matter how it is measured, stresses the ways in which CSCs differ from bulk cancer cells as well as from physiological stem cells. The cancer stemness phenomenon is of considerable clinical importance and significance because it prognosticates that successful anticancer therapy must involve strategies that will eradicate CSCs, as these cells are able to dominate any residual tumour cells that survive conventional anticancer therapies.

#### **3.2. Determinants and signatures**

In the past, the cancer stemness model was widely seen as a static one which suggested a stable CSC population and a hierarchical organisation of cell division and differentiation. In recent times, however, the cancer stemness model has evolved into a dynamic one where CSCs are rather accepted as a functional subpopulation of cancer cells and can also be formed by the process of dedifferentiation from mature cancer cells under proper environmental conditions [47]. Accumulating data have revealed that cancer stemness is governed by genetic changes (such as oncogene activation and oncosuppressor gene inactivation), epigenetic changes (such as miRNA targeting and promoter DNA hypomethylation/hypermethylation) and metabolic changes (such as the shift to aerobic glycolysis) concomitant with changes in the tumour microenvironment, especially the CSC niche (Figure 1). These changes are a prerequisite for the oncogenic transformation and cellular (nuclear and metabolic) reprogramming of non-CSCs to CSCs and precipitate a spectrum of drastic cellular consequences, including overpro‐ duction of certain oncoproteins, disruption of certain oncosuppressor proteins, upregulation/ reactivation of telomerase, reactivation of the epithelial-to-mesenchymal transition (EMT) programme, modulation of energy metabolism, stimulation of a number of embryonic/ oncogenic signalling pathways, and differential expression of several microRNAs (miRNAs).

in tumour initiation and progression, spreading, therapy resistance, and recurrence, all of

CSCs have critical implications for nearly, if not quite, all types of cancers, including leukae‐ mias [24–26], lymphomas [27], melanomas [28, 29], sarcomas [30], and various carcinomas such as brain [31], skin [32], head and neck [33], lung [34, 35], liver [36], gastric [37], colorectal [38, 39], bladder [40], pancreatic [41, 42], prostate [43], breast [44] and ovarian [45] cancers. The functions assumed by CSCs in human cancers are diverse, ranging from sustaining tumour growth and dissemination to treatment failure and tumour relapse. The three clear-cut features that contribute to the aforementioned functions of CSCs, aka cancer stemness traits, are: (i) their unrestricted ability to self-renew; (ii) their aberrant ability to differentiate into mixed populations of tumour cells; and (iii) their high ability to transition from a proliferative to a quiescent state. In spite of the fact that these operational characteristics are, to a large extent, shared by both CSCs and physiological stem cells, CSCs are the ones that are known to be related to several malignant phenotypes, including induction of invasion and metastasis and resistance to apoptosis. In addition, CSCs are distinguished from bulk tumour cells by their capacity to form nonadherent spheres when cultured in stem cell media, their propensity to found fresh tumours when transplanted into severe combined immunodeficient mice and their expression of a selected repertoire of stem cell-surface markers [46]. Therefore, it is important here to realise that cancer stemness, no matter how it is measured, stresses the ways in which CSCs differ from bulk cancer cells as well as from physiological stem cells. The cancer stemness phenomenon is of considerable clinical importance and significance because it prognosticates that successful anticancer therapy must involve strategies that will eradicate CSCs, as these cells are able to dominate any residual tumour cells that survive conventional anticancer

In the past, the cancer stemness model was widely seen as a static one which suggested a stable CSC population and a hierarchical organisation of cell division and differentiation. In recent times, however, the cancer stemness model has evolved into a dynamic one where CSCs are rather accepted as a functional subpopulation of cancer cells and can also be formed by the process of dedifferentiation from mature cancer cells under proper environmental conditions [47]. Accumulating data have revealed that cancer stemness is governed by genetic changes (such as oncogene activation and oncosuppressor gene inactivation), epigenetic changes (such as miRNA targeting and promoter DNA hypomethylation/hypermethylation) and metabolic changes (such as the shift to aerobic glycolysis) concomitant with changes in the tumour microenvironment, especially the CSC niche (Figure 1). These changes are a prerequisite for the oncogenic transformation and cellular (nuclear and metabolic) reprogramming of non-CSCs to CSCs and precipitate a spectrum of drastic cellular consequences, including overpro‐ duction of certain oncoproteins, disruption of certain oncosuppressor proteins, upregulation/ reactivation of telomerase, reactivation of the epithelial-to-mesenchymal transition (EMT)

which lead to poor prognosis.

190 Telomere - A Complex End of a Chromosome

therapies.

**3.2. Determinants and signatures**

**3.1. Definitions and measurements**

**Figure 1. How do CSCs arise?** The joint impact of genetic, epigenetic, metabolic and microenvironmental factors is believed to determine the conversion of non-CSCs (which could be normal stem cells, mature cancer cells, or others) to CSCs. This process somehow is a dynamic one and CSCs are a functional and not merely territorial subpopulation of cancer cells.

Disruption of diverse oncosuppressor proteins with antiproliferative, prodifferentiative and/or proapoptotic effects accounts for an early molecular event accompanying the emer‐ gence of cancer stemness traits. p53, pRB, PTEN, and p16INK4A are by far among the most commonly inactivated oncosuppressor proteins in advanced human tumours [48]. Their inactivation permits premalignant cells to avoid replicative senescence, apoptosis, or both, thereby continuing to divide and accumulating further tumourigenic alterations like genomic (chromosomal) instability that follows telomere erosion [49]. The subsequent upregulation/ reactivation of telomerase compensates for telomere erosion (which would otherwise trigger entry of cells into a period of crisis with massive cell death), suppressing genomic (chromo‐ somal) instability and allowing premalignant cells to proliferate for a virtually infinite number of cell divisions. Additionally, and surprisingly, there is accumulating evidence that telomer‐ ase upregulation/reactivation provides susceptible cells with cancer stemness traits. The many functions of telomerase in the development and maintenance of cancer stemness will be addressed in more detail in the next sections of this chapter.

A further means by which non-CSCs acquire cancer stemness traits is through the EMT process. Physiologically, EMT causes cells to change from a stationary epithelial to a motile mesen‐ chymal morphology, thereby allowing for wound healing, tissue regeneration and organ fibrosis in adults and cell migration and tissue remodelling in developing embryos [50]. In the context of epithelium-derived carcinoma, however, the reactivation of the EMT programme contributes to the evolution of primary tumours towards increasingly aggressive phenotypes. The complex molecular, cellular and morphological alterations linked to pathological EMT are generally mediated by the joint action of the signals from the tumour microenvironment that induce the EMT programme (for example, TGF-β signalling, inflammatory cytokines, and hypoxia), the transcription factors that coordinate the EMT programme (for example, SNAI-1, SNAI-2, ZEB-1, ZEB-2, TWIST-1, and TWIST-2), and the effector proteins that execute the EMT programme (for example, low levels of E-cadherin and high levels of vimentin, N-cadherin, fibronectin, CD44, and MMPs) [51]. Such cooperation between the cell-extrinsic signals and the cell-intrinsic regulators is fully important and primarily responsible for endowing epithelial tumour cells with CSC-like properties, ranging from cell motility to invasiveness to cell survival, which are indispensable to metastatic dissemination from the primary tumour site, secondary tumour growth at a distant site, and resistance to therapy, respectively. Although CSCs originating from bulk tumour cells within epithelium-derived carcinomas achieve their final stemness state possibly *via* EMT, the degree to which they resemble or depart from CSCs originating from adult stem cells has yet to be fully explored.

Metabolic reprogramming is also an obvious mechanism of intervening and redirecting the cell fate of differentiated (normal or non-CSC tumour) cells. Traditionally, energy metabolism was widely accepted as a passive process that generated ATP and building blocks to meet the demands of the specialised cell types of the body in response to extra- and/or intracellular signals. Today, however, the modulation of energy metabolism and build-up of oncogenic metabolites are viewed as the harbingers of cancer stemness [52]. Typically, cancer cells are dependent more on glycolysis for energy production, even in the presence of sufficient oxygen to support oxidative phosphorylation. This phenomenon of aerobic glycolysis is commonly referred to as the Warburg effect and appears to fulfil the requirement of proliferating cancer cells to rapidly yield ATP and to provide anabolic substrates (such as amino acids, nucleotides, and phospholipids) for their daughter cells [53]. Besides, increased lactate generation during aerobic glycolysis provokes the acidification of the tumour microenvironment, ultimately giving rise to motile, invasive/metastatic and drug-resistant cells [54]. In agreement with this, a recent report confirmed and substantiated the need for a metabolic switch to glycolysis in the emergence of EMT-driven CSC-like characteristics in basal-like breast cancer cells [55]. Another report utilising nasopharyngeal carcinoma as a model system established that behaviourally-selected and accordingly-assayed CSCs, as distinct from their differentiated progenies, exhibit a metabolic shift from oxidative phosphorylation to glycolysis for ATP supply [56]. Nevertheless, contradictory evidence on the metabolic profile of CSCs has also been presented; two independent research groups reported that the bioenergetic and biosyn‐ thetic demands of quiescent/slow-cycling CSCs are likely to be met by oxidative phosphory‐ lation, not by glycolysis [57, 58].

CSCs maintain their prolonged residence in the stemness state through diverting and co-opting elegant signalling pathways that are normally active during embryonic development. The embryonic/oncogenic signalling pathways operating in CSCs include Notch, Hedgehog (HH), Wnt/β-catenin, cytokine receptor-mediated JAK/STAT, TNF-α receptor-mediated NF-κB, growth factor receptor (receptor tyrosine kinase)-mediated PI3K/AKT/mTOR, TGF-β/BMP receptor-mediated SMAD, and Hippo-YAP/TAZ [59–61]. Sustained activation of and crosstalk between these cascades ultimately enhance the expression of cell-surface proteins (for example, CD133, CD44, integrins, and CXCR4), prosurvival proteins (for example, BCL-2, BCL-XL, MCL-1, survivin, and MIC-1), induced pluripotency-associated transcription factors (for example, BMI-1, OCT-3/4, SOX-2, MYC, and NANOG), EMT-associated proteins (for example, MMPs, vimentin, N-cadherin, SNAI, TWIST, and ZEB), glycolysis-associated proteins (for example, GLUTs and glycolytic enzymes), treatment resistance-associated proteins (for example, GSH, ALDH1, ABCB1, ABCC1, ABCG2, CHK-1, and CHK-2), proan‐ giogenic factors (for example, VEGF and COX-2), and proinflammatory cytokines (for example, IL-6 and TNF-α) [60, 62, 63].

Lastly, several miRNAs have been observed to support the emergence of cancer stemness traits through targeting signalling elements and gene groups implicated in CSC biology. miRNAs are a fast-growing class of short (19 to 22 nt), noncoding, regulatory RNA molecules that customarily bind to the 3′-untranslated region (3′-UTR) of their target transcripts to induce translational repression, degradation, or destabilisation. Although miRNAs generally help regulate the transitions between different stages of development, they are also linked with tumourigenesis. As such, CSC-specific miRNA expression profiles may be useful for prog‐ nostic purposes. Those miRNAs that are highly expressed in CSCs of a specific tumour are termed oncomiRs; those that are excluded from CSCs of the same tumour are known as tumour-suppressor miRs. Breast CSCs were the first CSCs in which differential expression of miRNAs was demonstrated [64].


**2.2 Housing policies and real estate as drivers of disaster risk**

While we have referred to hazard exposure in informal territories, our findings in northern cities show that there is formal housing for high-income groups also settled in areas with tsunamis, floods, or landslides hazards. However, contrary to the situation of the informal territories, it is common for the exposure of high-income groups to be accepted, as they would have sufficient resources to face and overcome a disaster. This would explain the constant construction of private housing in contraindicated urban sectors, although it is worth wondering if people with lower incomes are actually living in safe areas. Some authors have identified the tendency in northern Chile to build social housing near risk areas [12, 30, 31]. Yet we would like to expose a particular example observed in the city of Antofagasta where informal territories, the construction of social housing, and the risk of disaster overlap.

After Santiago, Antofagasta is the second most expensive city to reside in Chile. On its surface, there are different types of housing with differentiated values according to square meters and number of rooms. Given the difficulties of buying a home due to its high value, renting is consolidated as one of the main forms of tenure, however, its monthly values can reach USD \$630 for a 45 m2 apartment, meanwhile, renting a 3-bedroom house can cost USD \$1056. This is also exacerbated by the social geography of the city, where rental values change according to location and accessibility to goods and services. As a result of these restrictions on access to housing for ownership, rent, or even sublease, some families see in peripheries an opportunity to self-build their houses. In terms of disaster risk, peripheral informal settlements are exposed to countless hazards given their location in the western part of the coastal mountain range, an area determined by a steep slope that is highly susceptible to floods and landslides [32]. Furthermore, any house that is built in this area must consider historical disaster events. The last significant episode was a mudflow that affected the whole city of Antofagasta in 1991, triggered by an intense rainfall that activated dry creeks, registering 92 fatalities and 16 missing people [32]. Despite this, these are areas where, in addition to informal settlements, the state has built social housing for vulnerable groups. There is a remarkable example of Altos del Arenal building, a social housing project developed in 2018.

Located in the northern area of the city of Antofagasta, Altos del Arenal is a social housing complex evaluated and approved by the regional housing and urbanization service (known as SERVIU), which allowed the construction of 50 apartments of 62 m2 , producing a six-floor tall building. Prior to its construction, it was defined that its residents would be some families living in Los Arenales, one of the largest informal settlements in the city. Candidates for relocation had to register in the overcoming camps program of the Ministry of Housing and Urbanism and apply for a housing subsidy to buy the apartments. However, as **Figures 1** and **2** show, the social housing complex was placed in the rear sector of Los Arenales, an informal settlement under

*Mobile Housing as an Initial Proposal to Manage Informal Territories Exposed to Disaster Risks DOI: http://dx.doi.org/10.5772/intechopen.108828*

**Figure 1.** *Altos del Arenal social housing complex, Antofagasta. Source. Authors.*

## **Figure 2.**

*Susceptibility to mass removal, altos del Arenal social housing complex, Antofagasta. Source. Authors.*

constant risk of eviction. Likewise, it is an area under high susceptibility to landslides, in addition to historical mudflow records.

From this, state's decision to build social housing in risk areas, **Figures 1** and **2** above, expose some challenges to face. First, it is necessary to rethink the scale of disaster risk and its link with territorial planning instruments. In Antofagasta, the current urban regulatory plan was created 20 years ago, requiring it to be updated especially in risk issues, since zoning depends on hazard exposure studies, which have not been properly developed. And second, it is also necessary to investigate the exposure to anthropic hazards, such as proximity to dumps, the presence of pests, and pollutants, among others, as an exercise to begin unraveling the sociopolitical construction of risk in informal territories.


*2.5. Incidence of Diseases and Disorders*

Fruit was harvested in line with commercial quality standards and was free from diseases and disorders. After 6 months of storage, the pears were assessed for the presence of physiological disorders and fungal diseases. Fruit showing symptoms of, respectively, physiological diseases (internal browning and senescent scald) and fungal diseases was counted for each treatment. The identified fungal diseases included: gray mold caused by *Botrytis cinerea* Pers., blue mold caused by *Penicillium* spp., bitter rot caused by *Colletrotrichum* spp., and brown rot caused by *Monilinia* spp. or *Sclerotinia fructigena.* Next, all pears were counted per box, and each box was treated as one repetition. The results were expressed as a percentage share of infected/damaged fruit in the total number of evaluated fruit.


**4. Conclusion**

Good rheological data are necessary for a proper optimization of the production using highly filled polymers and also for good simulations of the related production processes. Since simulation of a production process needs a good virtual model of this process, the correct understanding of the occurring physical effects is vital.

Rheology of highly filled polymers is the tool in place to gain such a deeper understanding. On the other hand, it is essential to have this understanding when measuring reliable rheological data of such materials, which show special and characteristic behavior like yield stress or dependency on the powder loading and shear rate.


**11. Ethic**

Although an increased understanding of how these and other genes influence response to nutrients should facilitate the progression of personalized nutrition, the ethical issues surrounding its routine use need careful consideration (Lovegrove & Gitau., 2008).

The study of nutrigenetics is in its infancy. Many studies published in this area have only considered one SNP in a single gene, with little consideration being given to multiple nutrient–gene–environmental interactions. Although this is scientifically valid, and invaluable for the elucidation of causative mechanisms in disease, multiple gene–nutrient– environment–gender interactions will be required for developing specific personalized nutritional advice. The collation of data in haplotype databases and biobanks is expensive and difficult to establish, but is a necessity if nutrigenetic research is to progress (Lovegrove & Gitau., 2008).

Standardized protocols in nutrigenetics are not yet established, the comparison of studies is challenging and conclusions are often difficult to draw. As discussed previously, studies are often retrospective in design and thus of insufficient power to detect nutrient–gene associations. Prospective genotyping increases the power to resolve these associations and should be used whenever possible. With any research, publication bias results in positive associations being reported more often than negative associations. This has applied to nutrigentic studies and created a false impression of the level of significance of many nutrient–gene interactions (Lovegrove & Gitau., 2008).

There are numerous ethical issues and unavoidable assumptions that need to be considered before personalized nutritional can become routine practice. First, it is important to consider

Future studies will have to be large in order to assess the effects of multiple polymorphisms, and will have to control for many factors other than diet. At present, it is premature to recommend the use of genotyping in the design of therapeutic diets. However, such studies may be useful in identifying the mechanisms by which dietary components influence lipid

However, current knowledge is still very limited and so is the potential benefit of its application to clinical practice. Thinking needs to evolve from simple scenarios (e. g., one single dietary component, a single nucleotide polymorphism and risk factor) to more realistic situations involving multiple interactions. One of the first situations where personalized nutrition is likely to be beneficial is in patients with dyslipidemia who require special intervention with dietary treatment. This process could be more efficient if the recommendations were carried out based on genetic and molecular knowledge. Moreover, adherence to dietary advice may increase when it is supported with information based on nutritional genomics, and a patient believes the advice is personalized. However, a number of important changes in the provision of health care are needed to achieve the potential

approach with greater integration among physicians, food and nutrition professionals, and

The ultimate goal of nutritional genomics is to provide sufficient knowledge to allow diagnosis and nutritional treatment recommendations based on an individual's genotype. Defining the interaction effects of nutrients and genes on complex phenotypes will be the challenge of this field of nutrition research for some time to come (Fisler & Warden, 2005;

Although an increased understanding of how these and other genes influence response to nutrients should facilitate the progression of personalized nutrition, the ethical issues

The study of nutrigenetics is in its infancy. Many studies published in this area have only considered one SNP in a single gene, with little consideration being given to multiple nutrient–gene–environmental interactions. Although this is scientifically valid, and invaluable for the elucidation of causative mechanisms in disease, multiple gene–nutrient– environment–gender interactions will be required for developing specific personalized nutritional advice. The collation of data in haplotype databases and biobanks is expensive and difficult to establish, but is a necessity if nutrigenetic research is to progress (Lovegrove

Standardized protocols in nutrigenetics are not yet established, the comparison of studies is challenging and conclusions are often difficult to draw. As discussed previously, studies are often retrospective in design and thus of insufficient power to detect nutrient–gene associations. Prospective genotyping increases the power to resolve these associations and should be used whenever possible. With any research, publication bias results in positive associations being reported more often than negative associations. This has applied to nutrigentic studies and created a false impression of the level of significance of many

There are numerous ethical issues and unavoidable assumptions that need to be considered before personalized nutritional can become routine practice. First, it is important to consider

surrounding its routine use need careful consideration (Lovegrove & Gitau., 2008).

levels (Masson & McNeill., 2005).

genetic counselors (Ordovas., 2006).

**11. Ethic**

& Gitau., 2008).

Gregori et al, 2011; Ordovas & Corella., 2004).

nutrient–gene interactions (Lovegrove & Gitau., 2008).

benefits associated with this concept, including a teamwork

whether the genetic tests and personalized food products would be affordable, cost-effective and socially acceptable. It is also of concern that only the well educated and affluent would benefit. The open accessibility of genetic information to third parties has major implications for the availability of health insurance and increased premiums (Lovegrove, Gitau, 2008).

Moreover, it is still unknown whether people will want to undertake genetic tests or even understand the concept of such technology. A survey was conducted by Cogent Research in 2003 on 1000 Americans in which 62% of respondents reported they had never heard of 'nutrigenomics'. However, if specific products did arise from nutrigenomic research, those interviewed did express interest in an in-depth well-being assessment and also a strong interest in vitamins, fortified foods and natural foods. More research is required to determine whether individuals would want to undergo such tests, and for understanding the value to the individual of an increased awareness of personalized nutrition regimens. There is already a large gap between the existing dietary guidelines and what people actually eat. Knowledge of being at higher than average risk of CVD may motivate people to actually make positive changes to their diets. However, genetic testing could undermine current healthy eating messages, by implying that only those with the 'risky gene' need to eat a healthy diet. These are important unanswered questions that must be addressed if personalized nutritional advice is ever to become part of mainstream disease prevention and treatment. It may be that the interactions between genotype–phenotype and the environment are just too complex to be properly understood from human dietary intervention studies (Lovegrove, Gitau, 2008).

There is resistance to the use and perceived effectiveness of personalized nutrition that is based on genomics, and whether this can offer a solution to diseases caused by a diet that is inappropriate for health (Canon & Leitzmann, 2005). It has been suggested that it may be more beneficial to use current risk factors as a basis for population screening and the management of CVD (McCluskey et al., 2007). There has also been dialogue on the social, economic and environmental causes of CVD, shifting the emphasis away from dietary intake to food manufacturing as being more effective in disease management (Canon & Leitzmann, 2005; Lovegrove & Gitau., 2008).

Progression of knowledge in the fields of nutrient–gene interactions promises a future revolution in preventative health care. However, although there is increasing evidence for interactions between diets/nutrients, genes and environmental factors, there are inconsistencies in the evidence that will limit the application of nutrigenetics in diet-related disease in the immediate future. In addition to the need for adequately powered intervention studies, greater attention should be given to ethical issues, such as the public's acceptance of genetic testing and the economics of this relatively new science (Lovegrove & Gitau., 2008).

Ethical issues fall into a number of categories: (1) why nutrigenomics? Will it have important public health benefits? (2) questions about research, e. g. concerning the acquisition of information about individual genetic variation; (3) questions about who has access to this information, and its possible misuse; (4) the applications of this information in terms of public health policy, and the negotiation of the potential tension between the interests of the individual in relation to, for example, prevention of conditions such as obesity and allergy; (5) the appropriate ethical approach to the issues, e. g. the moral difference, if any, between therapy and enhancement in relation to individualised diets; whether the 'technological fix' is always appropriate, especially in the wider context of the

Nutrigenetics and Dyslipidemia 69

Fenech, M. (2008). Genome health nutrigenomics and nutrigenetics-dignosis and nutritional treatment of genome damage on an individual basis. *FdChem Tox*. 46 (4):1365-70. Fisler, J S. & Warden, C H. (2005). Dietary fat and genotype: toward individualized

Friedlander, Y. & Leitersdorf, E. & Vecsler, R. et al. (2000). The contribution of candidate

Frikke-Schmidt R. (2000). Context-dependent and invariant associations between APOE

Gillies, P J. (2003). Nutrigenomics : the Rubicon of molecular nutrition. *J am Diet Ass*. 103 (12

Gregori, D. & Foltran, F. & Verduci, E. & Ballali, S. & Franchin, L. & Ghidina, M. &. Halpern,

Hank Juo, S-H. & Wyszynski, D F. & Beaty, T H. & Huang, H-Y. & Bailey-W, J E. (1999).

Heilbronn a, L. K., & Noakes b, M. & Morris a, A. M. & Kind b, K. L. & Clifton, P. M. (2000).

Hockey, K. J. & Anderson, R. A. & Cook, V. R. & Hantgan, R. R. & Weinberg R. B.

Isasi, CR. & Couch, SC. & Deckelbaum, RJ. et al. (2000). The apolipoprotein e2 allele is

Jarvik, GP., & Goode, EL. & Austin, MA, et al. (1997). Evidence that the apolipoprotein

Kobayashi, J. & Saito, Y. & Taira, K. & Hikita, M. & , Takahashi, K. & Bujo, H. & Morisaki, N.

Kusmann, M. & Fsy, L B. (2008). nutrigenomics and personalized nutrition:science and

Loktionov, A. & Scollen, S. & McKeown, N. & Bingham S. (2000). Gene±nutrient

1256.

suppl 2):50-55.

discussion 249-52**.**

*Atherosclerosis*. 152:239±248.

*Scand J Clin Lab Invest*. 60 (Suppl. 233):3±26.

*Journal of Medical Genetics.* 82 (3):235-241.

Still Need Them?. *J Nutrigenet Nutrigenomic.* 4 (1):25-35.

plasma triglyceride clearance. *J. Lipid Res.* . 42: 211–217.

University Biomarkers Study. *Pediatrics*; 106:568±575.

analysis. *Am J Hum Genet*; 61:171±181.

subjects in Japan. *Atherosclerosis*. 154; 539–546.

concept. *Personalized Medicine*. 5 (5):447-455.

living individuals. *Br J Nutr*. 84:885±890.

prescriptions for lifestyle changes. *American Journal of Clinical Nutrition*. 81 (6): 1255-

genes to the response of plasma lipids and lipoproteins to dietary challenge.

genotype and levels of lipoproteins and risk of ischemic heart disease: a review.

G M. & Giovannini, M. (2011). A Genetic Perspective on Nutritional Profiles: Do We

Mild association between the A/G polymorphism in the promoter of the apolipoprotein A-I gene and apolipoprotein A-I levels: A meta- analysis. *American*

360His polymorphism of the apolipoproteinA-IV gene and plasma lipid response to energy restricted diets in overweight subjects. *Atherosclerosis.* 150: 187–192. Hernell, O. & West. C. (2008). Do we need personalized recommendations for infants at risk

of developing disease? *Nestle Nutr Workshop ser Pediatr Program.* 62:239-49;

(2001)Effect of the apolipoprotein A-IV Q360H polymorphism on postprandial

associated with an anti- atherogenic lipoprotein profile in children: the Columbia

Egenotype effects on lipid levels can change with age in males: a longitudinal

& Saito, Y. (2001). Effect of apolipoprotein E3:4 phenotype on postprandial triglycerides and retinyl palmitate metabolism in plasma from hyperlipidemic

interactions: dietary behavior associated with high coronary heart disease risk particularly affects serum LDL cholesterol in apolipoprotein E (4-carrying free-

purported lack of public confidence in science, which has special resonance in the sphere of nutrition (Chadwick., 2004).

#### **12. References**

Amouyel, P. (2000). Genomics and cardiovascular diseases. *Bull Acad Natl Med*. 184 (7):1431-8.

purported lack of public confidence in science, which has special resonance in the sphere of

Amouyel, P. (2000). Genomics and cardiovascular diseases. *Bull Acad Natl Med*. 184 (7):1431-8. Boekholdt, SM. & Kuivenhoven, JA. & Hovingh, GK. & Jukema, JW. & Kastelein, JJ. & van,

Campos, H. & D'Agostino, M. & Ordovas, JM. (2001). Gene±diet interactions and plasma

Canon, G. & Leitzmann, C. (2005). The new nutrition science Progect. *Public Health Nutr*. : 8

Carmena-Ramon, R. & Ascaso, J F. & Real, J T. & Ordovas, J M. (1998 ). Genetic Variation at

Chadwick, R. (2004). Nutrigenomics, individualism and public health. *Proc Nutr Soc*. 63

Clifton, P. & Kind, K. & Jones, C. & , Noakes, M. (1997). Response to dietary fat and cholesterol and genetic polymorphisms. *Clin Exp Pharmacol Physiol*;24:A21–5. Corella, D. & Tucker, K. & Lahoz, C. & Coltell, O. & Cupples, L A. & Wilson, P WF. &

Couch, SC. & Isasi, CR. & Karmally. W, et al. (2000). Predictors of postprandial

Debra A. K. (2008). Medical Nutrition Therapy For Cardiovascular Disease. In Mahan KL,

DeBusk, R M. (2009). Nutritional Genomics: The Foundation for Personalized Nutrition,

Dreon, DM. & Fernstrom, HA. & Miller, B. & , Krauss, RM. (1995). Apolipoprotein E isoform

Engler, M B. (2009). Nutrigenomics in Cardiovascular Disease: Implications for the Future.

Farhud, D. & Shalileh, M. (2008). Phenylketonuria and its Dietary Therapy in Children. Iran

Farhud, DD. & Zarif Yeganeh, M. & Zarif Yeganeh, M. *(2010)*. Nutrigenomics and

*Arteriosclerosis, Thrombosis, and Vascular Biology.* 18:1266- 1274.

Study. *American Journal of Clinical Nutrition*. 73 (4): 736-745.

Tol A. (2004). CETP gene variation: relation to lipid parameters and cardiovascular

lipoproteins: role of apolipoprotein E and habitual saturated fat intake. *Genet*

the ApoA-IV Gene Locus and Response to Diet in Familial Hypercholesterolemia.

Schaefer, E J. & Ordovas, J M. (2001). Alcohol drinking determines the effect of the *APOE* locus on LDL-cholesterol concentrations in men: the Framingham Offspring

triacylglycerol response in children: the Columbia University Biomarkers Study.

Escoott- Stump S (eds). *Krause's Food Nutrition and Diet Therapy*. Suanders. 12th ed. Philadelphia. 833-864. DeBusk, R. . (2008). Nutritional genomic. In Mahan KL, Escoott-Stump S (eds). *Krause's Food Nutrition and Diet Therapy*. 12th ed. Suanders.

What Is Nutritional Genomicsl. *In Advanced nutrition and human metabolism.* Sareen S. G, Jack L. Smith, James L. G. FIFTH EDITION*. WADSWORTH-CENGAGE*

phenotype and LDL subclass response to a reduced-fat diet. *Arterioscler Thromb*

nutrition (Chadwick., 2004).

risk. *Curr Opin Lipidol.* 15 (4):393-8.

*Epidemiol*; 20:117±128.

*Am J Clin Nutr*. 72:1119±1127.

Philadelphia. Pp: 364- 382.

*Learning*". USA. pp 29-32.

J Pediatr. 18 (Suppl 1):88-98.

El-Sohemy, A. (2007). Nutrigenetics. *Forum Nutr*. 60:25-30.

*Progress in Cardiovascular Nursing.* 24 (4): 190–195.

Nutrigenetics*. Iranian J Public Health.* 39 (4) :1-14.

*Vasc Biol*; 15:105±111.

(6A):673-694.

(1):161-6.

**12. References**

Nutrigenetics and Dyslipidemia 71

Perez-Martinez, P. & Garcia-Rios, A. & Delgado-Lista, J. & Perez-Jimenez, F. & Lopez-

of genetic variation in responsiveness to diet. *Am J Clin Nutr*. 71:713–24. Rideout, T C. (2011). Getting personal: considering variable interindividual responsiveness to dietary lipid-lowering therapies. *Current Opinion in Lipidology.* 22 (1 ): 37–42 . Rimbach, G. & Minihane, A M. (2009). nutrigenetics and personalized nutrition:how far

Rubin, J. & Berglund, L. (2002). Apolipoprotein E and diets: a case of gene-nutrient

Ryan-Harshman, M. & Vogel, E. & Jones-Taggart, H. & Green-Johnson, J. & Castle, D. &

Shalileh, M. & , Shidfar, F. & Eghtesadi, SH. & Haghani, H. & Heidari, I. (2009). The Effects

Shalileh, M. & , Shidfar, F. & Haghani, H. & Eghtesadi, SH. & Heidari, I. (2010). The

Sutton, A J. & Duval, S J. & Tweedie, R L. & Abrams, K R. & Jones, D R. *(2000).* Empirical assessment of effect of publication bias on meta-analyse**.** *BMJ.* 320 : 1574

Tai, E S. & Corella, D. & Deurenberg-Yap, M. & Cutter, J. & Chew Kai, S. & Tan, C E. &

Wallace, A J. & E Humphries, S. & Fisher, R M. & Mann, J I. & Chisholm, A. & Sutherland,

Weinberg, R B. & Geissinger, B W. & Kasala, K. & Hockey, K J. & James G, T. & Easter, L. &

change in the nature of dietary fat. *Atherosclerosis*. ; 149 (2) : 387-394. Weggemans, RM. & Zock, PL. & Ordovas JM, et al. (2001). Apoprotein E genotype and the

Ordovas, J M. (2003). Dietary Fat Interacts with the -514C>T Polymorphism in the Hepatic Lipase Gene Promoter on Plasma Lipid Profiles in a Multiethnic Asian Population: The 1998 Singapore National Health Survey. *J Nutr*. 133:3399-3408. Tikkanen, M. & Huttunen, JK. & Pajukanta, PE. & Pietinen, P. (1995). Apolipoprotein E

polymorphism and dietary plasma cholesterol response. *Can J Cardiol.* 11:93G-96G.

W H F. (2000). Genetic factors associated with response of LDL subfractions to

response of serum cholesterol to dietary fat, cholesterol and cafestol. *Atherosclerosis*.

Crouse, R. (2000). Effect of apolipoprotein A-IV genotype and dietary fat on

resistance in obese adults receiving low calorie diet. JRMS; 15 (4):1-11. Sheweta, B. & Deepak, M. & BS Khatkar. (2011). Nutrigenomics :The emerging face of nutrition. *International Journal of Current Research and Review*. 3 (6) :108-113. Subbiah, M T R. (2007). Nutrigenetics and nutraceuticals: the next wave riding on

interaction? *Current Opinion in Lipidology*. 13:25±32.

Restricted Diet*. Iranian J Publ Health. 38 (4):10-20*.

personalized medicine. *Transl res*. 149 (2):55-61.

URL: en. wikipedia. org/wiki/Dyslipidemia.

154:547±555.

practice. Kristin Anderson Can J Diet Pract Res. 69 (4):177-82.

Svacina, S. (2007)Nutrigenetics and nutrigenomics. *Cas Lek Cesk*. 146 (11):837-9.

URL : http://www. who. int/mediacentre/factsheets/fs317/en/index. html

cholesterol absorption in humans. *J Lipid Res*. 41: 2035–2041.

*Society*. 68 (2): 162-172.

Miranda, J. (2011). Nutrigenetics of the postprandial lipoprotein metabolism: evidences from human intervention studies. *Curr Vasc Pharmacol*. 9 (3):287-91. Rantala, M. & Rantala, TT. & Savolainen, MJ. & Friedlander, Y. & Kesaniemi, YA. (2000).

Apolipoprotein B gene polymorphisms and serum lipids: meta-analysis of the role

have we progressed and are we likely to get there? *Proceedings of the Nutrition*

Austin, Z. & Anderson, K. (2008). Nutritional genomics and dietetic professional

of Calcium Supplement on Serum Lipoprotein in Obese Adults Receiving Energy

influence of calcium supplement on body composition, weight loss and insulin

URL:www. jlr. org. on July 26.

Lopez-Miranda, J. & Ordovas, JM. & Mata, P, et al. (1994). Effect of apolipoprotein E

Lovegrove, J. A. & Gitau, R. (2008). Personalized nutrition for the prevention of

Mahley, RW. & PeÂ, pin J. & Erhan, Paraoglu K, et al. (2000). Low levels of high density

Masson, L F. & McNeill, G. & Avenell, A. (2003). Genetic variation and the lipid response to

Masson, L F. & McNeill, G. (2005). The effect of genetic variation on the lipid response to dietary change: recent findings. *Current Opinion in Lipidology*. 16 (1):61-67. McCluskey, S. & Baker, D. & Perey, D. & Lewis, P. & Middleton, E. (2007). Reduction in

Merkel, M. & Eckel, R H. & Goldberg, I J. (2011). Lipoprotein lipase: genetics, lipid uptake

Miggiano, GA. & De Sanctis, R. (2006). Nutritional genomics: toward a personalized diet.

Musunuru, K. (2010). Atherogenic Dyslipidemia: Cardiovascular Risk and Dietary

Mutch, D M. & Wahli, W. & Gary, W. (2005). Nutrigenomics and nutrigenetics: the

Ordovas, J. M. (2002). Gene-diet interaction and plasma lipid responses to dietary

a.Ommen, B V. (2004). Nutrigenomics: Exploiting Systems Biology in the Nutrition and

b.Ordovas, JM. (2004). The quest for cardiovascular health in the genomic era : nutrigenetics

Ordovas JM, Corella D. (2004). Nutritional genomics. *Annu Rev Genomics Hum Genet*. 5:71-

Ordovas JM, Mooser V. (2004). Nutrigenomics and nutrigenetics. *Curr Opin Lipidol*. 15

Ordovas, J. M. (2006). Nutrigenetics, plasma lipids and cardiovascular risk. *Journal Of The*

Ordovas. J M. & Kaput, J. & Corella, D. (2007). Nutrition in the genomics era:

Ordovas, J M. (2009). Genetic influences on blood lipids and cardiovascular disease risk:

Cardiovascular disease risk and the Mediterranean diet. *Molecular Nutrition & Food*

tools for primary prevention. *American Journal of Clinical Nutrition*. 89 (5):1509S-

emerging faces of nutrition. *The FASEB Journal.* 19:1602-1616.

intervention. *Biochemical Society Transactions.* 30: (68–73).

and plasma lipoproteins*. Proc Nutr Soc*. 63 (1):145-152.

*American Dietetic Association*. 106 (7):1074-81.

*J Lipid Res*. 35:1965–75.

*Dietetics*. 21 (4) : 306–316.

(5): 1098-1111.

and regulation

118.

(2):101-8.

1507S.

URL:www. jlr. org. on July 26.

Intervention. *Lipids.* 45:907–914.

Health Arenas. *Nutrition*. 20:4–8.

*Research*. 51 (10) :1293–1299.

*Clin Ter*. 157 (4):355-61.

genotype. *J Lipid Res*. 41:1290±1301.

longitudinal study. *J Public Heaht (oxf )*. 29:379-387.

phenotype on diet-induced lowering of plasma low density lipoprotein cholesterol.

cardiovascular disease: a future perspective. *Journal of Human Nutrition and*

lipoproteins in Turks, a population with elevated hepatic lipase: high density lipoprotein characterization and gender-specific effects of apolipoprotein E

dietary intervention: a systematic review. *American Journal of Clinical Nutrition*. 77

cardiovascular risk in association with population screening : a 10- year

**4**

*Iran*

**Impact of Climate Change and**

Roya Kelishadi1 and Parinaz Poursafa2

*Isfahan University of Medical Sciences, Isfahan,*

*Isfahan University of Medical Sciences, Isfahan,*

*2Environment Research Center,*

**Air Pollution on Dyslipidemia and**

*1Faculty of Medicine & Child Health Promotion Research Center,*

**the Components of Metabolic Syndrome**

Environmental factors, notably climate change and air pollution influence health before conception, and continue during pregnancy, childhood, and adolescence. Experts have suggested that such health hazards may represent the greatest public health challenge humanity has faced. The accumulation of greenhouse gases such as carbon dioxide, primarily from burning fossil fuels results in warming, which has an impact on air pollution, particularly on levels of ozone and particulates. Heat-related health effects include increased rates of pregnancy complications, pre-eclampsia, eclampsia, low birth weight, renal effects, vector-borne diseases as malaria and dengue; increased diarrheal and respiratory disease, food insecurity, decreased quality of foods (notably grains), malnutrition, water scarcity, exposures to toxic chemicals, worsened poverty, natural disasters, and population displacement. Air pollution has many adverse health effects, which would have long-term impact on the components of the metabolic syndrome. In addition to short-term effects as premature labor, intrauterine growth retardation, neonatal and infant mortality rate, malignancies (notably leukemia and Hodgkin lymphoma), respiratory diseases, allergic disorders and anemia, exposure to criteria air pollutants from early life might be associated with dyslipidemia, increase in stress oxidative, inflammation and endothelial dysfunction which in turn might have long-term effects on chronic non-

Air pollution is a mixture of solid particles and gases in the air. The six common and hazardous air pollutants consist of particulate matter, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead; of which, particle pollution and groundlevel ozone are the most widespread health hazards (Samet & Krewski, 2007; Chen & Kan,

**2. Environmental factors: Climate change and air pollution**

**1. Introduction**

communicable diseases.

**2.1 Air pollutants**

2008).