**About the Editors**

#### **Majid Ali**

Dr. Majid is currently working as a Professor at the Capital University of Science and Technology, Islamabad, Pakistan. He has over 19 years of teaching, research, and professional industry experience. He obtained his B.Sc. in Civil Engineering with a gold medal, his M.Sc. with the highest position from the University of Engineering and Technology, Taxila, and his Ph.D. with five impact factor journal publications from the University of Auckland, New Zealand. He has published a total of 176 publications, including 36 Impact Factor journal papers with a cumulative ISI impact factor of more than 220. Before joining academia, for ten years he worked as a Structural Design Engineer with NESPAK (one of the leading consultants in Pakistan). His research interests include construction materials, fiber composites, and earthquake-resistant design.

#### **Muhammad Ashraf Javid**

Dr. Javid works as an Associate Professor at the Capital University of Science and Technology, slamabad, Pakistan. He completed his B.Sc. in Civil Engineering and M.Sc. in Transportation Engineering from the University of Engineering and Technology, Lahore, Pakistan, and earned his Ph.D. in Civil Engineering from Yokohama National University, Japan. He has 16 years of teaching and research experience in the civil engineering field and has worked at both national and international universities. His research areas mainly include passenger travel behavior, transportation demand management policies, the interaction between public transport services and customers, shared mobility and economy, and traffic safety. He has published a total of 55 journal publications and 21 conference proceedings, and his cumulative publication ISI impact factor is more than 70.

#### **Shaheed Ullah**

Engr. Shaheed Ullah works as lecturer at the Capital University of Science and Technology, Islamabad, Pakistan. He completed his B.Sc. in Civil Engineering and M.Sc. in Structural Engineering at the UET Peshawar and MCE NUST Campus, Risalpur, respectively. He has five years of teaching experience and has research expertise in construction materials.

#### **Iqbal Ahmad**

Engr. Iqbal works as a lecturer at the Capital University of Science and Technology, Islamabad, Pakistan. He is a Structural Engineer with a Master's degree in Structural Engineering. He has more than 9 years of teaching and industry experience. His research themes include finite element modeling, engineering materials, and numerical analysis.

### *Editorial* **Statement of Peer Review †**

#### **Majid Ali \*, Muhammad Ashraf Javid, Shaheed Ullah and Iqbal Ahmad**

Department of Civil Engineering, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan<EMAIL_ADDRESS>(M.A.J<EMAIL_ADDRESS>(S.U<EMAIL_ADDRESS>(I.A.)

In submitting the proceedings of the 5th Conference on Sustainability in Civil Engineering (CSCE) to *Engineering Proceedings*, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal.

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

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**Citation:** Ali, M.; Javid, M.A.; Ullah, S.; Ahmad, I. Statement of Peer Review. *Eng. Proc.* **2023**, *44*, 8. https://doi.org/10.3390/ engproc2023044008

Published: 23 August 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).


**6. Treatment consideration for non-lesional epilepsy**

There is always difficulty in identification of the epileptogenic zone in nonlesional neocortical epilepsy. Seizure free outcomes are about 55% for non-lesional temporal lobe epilepsy and 43% for non-lesional extratemporal lobe epilepsy patients. Concordance with two or more presurgical evaluations including interictal EEG, ictal EEG, FDG-PET, and ictal SPECT was significantly related to a seizure-free outcome. Another study showed that 38% of non-lesional epilepsy patients had an excellent outcome after resective epilepsy surgery after long-term intracranial EEG. In temporal lobe epilepsy with MRI negative and PET positive findings, surgery could achieve Class I surgical outcomes at postoperative 2 years in about 82% [59, 60].

#### **7. Factors related to failure in epilepsy surgery**

Failure of epilepsy surgery may be caused by wrong localization of the epileptogenic zone, very widespread epileptogenic zones and very limited resection of the suspected epileptogenic zone.

In patient after mesial temporal resection, seizure may arise from neocortical regions instead of from residual hippocampal structure. This may imply the existence of regional epileptogenicity. Hippocampus represents the area of cortex with

#### *Presurgical Evaluation of Epilepsy Surgery DOI: http://dx.doi.org/10.5772/intechopen.93602*

the lowest threshold for seizure generation and the surrounding neocortical tissue also exhibiting epileptogenicity then becomes the site of ictal onset. About 25% of patients with seizure relapse after mesial temporal sclerosis may have seizure onset in the contralateral temporal region.

Extensive reevaluation of these patients is suggested for consideration of reoperation if epileptogenic focus can be localized.


**2. Experimental Program**

A total of 30 specimens for the short-lapped-rebar splices were designed considering the influences of the lapped length and the rebar diameter. The pull-out tests were then carried out to investigate the bond–slip behavior of the short-lapped-rebar splices.


**2. Mechanical, thermomechanical and thermochemical pretreatments**

An early, essential mechanical pretreatment step is comminution, or mechanical particle size reduction, to transform the biomass from its native state into a suitable substrate for further pretreatment and energy production [17]. This step is often not considered in the energy balance of biofuels processes, but it is important to keep in mind that particle size reduction involves energy input that can influence the effective energy yield of these processes [22]. While smaller particle sizes are often considered to be more desirable for yields of fermentable sugars, sizes smaller than about 0.4-0.5 mm provide no additional benefit [17, 23], and the process becomes economically unfeasible at even smaller particle sizes [22]. Methods for mechanical size reduction include wet milling, dry milling, ball milling or vibratory ball milling, and other forms of chipping and grinding of biomass [4, 17]. Regardless of the method employed, particle size reduction requires energy input; therefore, strategies that facilitate the production of biomass in the proper size range while minimizing energy input will provide positive benefits to the overall economics of biofuels processes.

A wide range of options is available for preparing ground biomass for further processing. One of the most common and simple technologies for rendering the carbohydrate fraction available for biofuels production is the application of a dilute solution of sulfuric acid (0.5%-2%) at temperatures of 140°C – 180°C with residence times of 10-30 minutes [24]. This process leaves a residue that is depleted in hemicellulose but retains most of the cellulose intact, making it an ideal substrate for enzymatic hydrolysis to yield fermentable sugars for ethanol production. There is a range of conditions for acid hydrolysis that will result in more or less carbohydrate remaining in the solid fraction, with the most severe conditions used to completely degrade the carbohydrate fraction for the determination of cell wall carbohydrate composition [25]. Harsher conditions (e.g. higher acid concentration and temperature), while resulting in a substrate that is highly digestible with enzymes to generate fermentable sugars, also result in a higher yield of compounds derived from pentoses (furfural), hexoses (5-hydroxymethylfur‐ fural) and lignin (low molecular weight phenolic compounds) that are inhibitory to subsequent fermentation by ethanologenic yeasts [26]. The mathematical concept of combined severity, which combines the various factors that define acid hydrolysis conditions (e.g. temperature, residence time, pH), allows objective comparisons between different conditions that enables the determination of optimal conditions for a given substrate [26]; however, doubts have been raised about its accuracy [17].

biofuel production by fermentation [4]. A successful combination of microwave and chemical pretreatments in a microwave-acid-alkali-hydrogen peroxide sequence resulted in efficient enzymatic saccharification of rice straw [36]. A related pretreatment option that has been exploited to improve the enzymatic digestibility of switchgrass is the use of radio frequency heating in combination with alkali; this treatment has the key advantage of allowing a much higher solids content than conventional heating [37]. Irradiation of biomass can also enhance

Pretreatment of Lignocellulosic Biomass Using Microorganisms: Approaches, Advantages, and Limitations

http://dx.doi.org/10.5772/55088

185

A number of other pretreatment options exist, including ammonia fiber explosion (AFEX), liquid hot water, alkalai/wet oxidative pretreament, and others; several recent reviews discuss these processes and their advantages and disadvantages in detail [4, 17-20, 23, 30, 38, 39]. Regardless of the strategy employed, a common feature of any pretreatment option is that energy input is required. Pretreatment is a major part of the overall operating expense and energy efficiency of any biofuels process, and, while essential, typically accounts for over 30% of the costs of biorefinery operation [40, 41]. Strategies to reduce these costs will have a major

Microorganisms have evolved a capacity to modify and access lignocellulosic biomass to meet their metabolic needs. The exploitation of this capacity offers a natural, low-input means for preparing biomass for biofuels processes. Natural modification and degradation of the lignin component in particular can reduce the severity requirements of subsequent thermochemical pretreatment steps. For example, Itoh et al. [42] used a variety of lignin-degrading white-rot fungi to treat wood chips prior to extracting lignin by an organosolv method, and demon‐ strated that improved ethanol yields were obtained from the solid fraction along with a 15% savings in electricity use. Similarly, brown-rot fungal species *Coniophora puteana* and *Postia placenta* have been successfully used to improve glucose yields upon enzymatic saccharifica‐ tion of pine, acting as a complete replacement for thermomechanical pretreatments [43]. While it is clear that it is possible to exploit the metabolic capabilities of microorganisms to facilitate biofuels production, the very wide taxonomic array of microorganisms that modify or degrade lignocellulose presents a tremendous variety of choices for implementing such a strategy. Each

One approach for applying the power of microbial metabolism to the challenges of biofuel production involves ensiling, which is a commonly used means for enhancing the digestibility of forage and other biomass for ruminants [44, 45]. The process of ensiling exploits the capacity of naturally occurring bacteria, mostly Lactobacillaceae, to ferment the sugars within ligno‐ cellulosic residues and produce a substrate that is more easily digested by ruminal microor‐ ganisms. While these bacterial consortia lack the ability to substantially degrade the lignin component, the changes effected on the biomass can improve yields of fermentable sugars

impact on the energy balance and economic sustainability of biorefineries.

methane production by anaerobic digestion [12].

**3. Biological pretreatments**

**3.1. Microbial consortia**

approach carries its own advantages and challenges.

Another highly effective pretreatment strategy is steam explosion, in which biomass is briefly heated to high temperatures (~200°C) under high pressure, then subjected to a rapid pressure drop that renders the biomass more penetrable by enzymes for subsequent hydrolysis [18]. In some cases, steam explosion is enhanced by the addition of an acid catalyst such as sulfuric acid [27]. For lignocellulosic agricultural residues, steam explosion under optimized condi‐ tions has been shown to be an effective pretreatment strategy for enzymatic saccharification [28]. Steam explosion has also been successfully used in combination with other physiochem‐ ical pretreatments such as acid/water impregnation of cereal straws [29]. Both of the latter studies resulted in the release of hemicellulose-derived pentose oligomers into the liquid fraction, and it was suggested that the use of ethanologenic strains capable of converting these pentoses into ethanol would further improve overall process efficiency [28]. Other assessments have suggested that the hemicellulose fraction would be more efficiently converted to other value-added products rather than ethanol using post-treatment enzyme addition or further acid hydrolysis [30].

Organosolv is a process by which the lignin fraction is chemically modified and essentially removed from biomass using high-temperature extraction with alcohols such as methanol or ethanol or other solvents, sometimes with dilute acid (e.g. hydrochloric or sulfuric acid) as a catalyst [17]. While organosolv processes require a solvent recovery step to be economical and efficient, they provide a robust means of generating three streams of potential products: an extracted, modified lignin component, a hemicellulose-enriched aqueous phase, and a residue that is highly enriched in cellulose and an excellent substrate for the production of biofuels by enzymatic saccharification followed by bacterial or yeast fermentation. Organosolv is one of the pretreatment options that results in a fraction containing chemically modified, low molecular weight lignin components. This stream has a good deal of product potential in addition to its possible use as a fuel for combustion to provide energy to the process [7, 31]. While organosolv is particularly suited to very lignin-rich feedstocks such as wood [32], there is increasing interest in using organosolv extractions for agricultural residues such as wheat straw and dedicated biofuels crops [33]. Goh et al. [34] optimized organosolv conditions for empty palm fruit bunch using combined severity calculations, with excellent results and the ability to accurately predict product stream yields.

Microwave pretreatment of biomass is another option that has been reported to improve subsequent enzymatic saccharification of rice straw [35]. Microwaves have the advantage of combining very rapid heating times with a lower energy input than conventional heating strategies. This irradiative pretreatment creates localized hotspots, which open up the lignocellulose composite molecule, thereby facilitating enzyme access for saccharification and biofuel production by fermentation [4]. A successful combination of microwave and chemical pretreatments in a microwave-acid-alkali-hydrogen peroxide sequence resulted in efficient enzymatic saccharification of rice straw [36]. A related pretreatment option that has been exploited to improve the enzymatic digestibility of switchgrass is the use of radio frequency heating in combination with alkali; this treatment has the key advantage of allowing a much higher solids content than conventional heating [37]. Irradiation of biomass can also enhance methane production by anaerobic digestion [12].

A number of other pretreatment options exist, including ammonia fiber explosion (AFEX), liquid hot water, alkalai/wet oxidative pretreament, and others; several recent reviews discuss these processes and their advantages and disadvantages in detail [4, 17-20, 23, 30, 38, 39]. Regardless of the strategy employed, a common feature of any pretreatment option is that energy input is required. Pretreatment is a major part of the overall operating expense and energy efficiency of any biofuels process, and, while essential, typically accounts for over 30% of the costs of biorefinery operation [40, 41]. Strategies to reduce these costs will have a major impact on the energy balance and economic sustainability of biorefineries.


Regarding the importance of the proposed illustrated algorithm, the present analysis yields an empirical model that incorporates a macro and micro view of predictive relationships between the independent, mediating, and outcome factors of the subjects' health in interaction with the contextual, organizational factors. In our view, this model has unquestionable epidemiological value for revealing probabilistic predictive relationships between personal and contextual factors, thereby enabling healthcare organizations to understand and make decisions regarding the detection of diseases that bring increased likelihood of others. It also enables large-scale assessment of the adequacy of resources deployed as a function of the pathologies analyzed, opening the way to cost-benefit analyses. Some previous analyses have contributed evidence in this line of work, using different methodologies (8, 46).

The results of this study are also relevant from the point of view of clinical management. Attention to the value of contextual elements (mainly managerial and organizational elements like stroke units) would unquestionably contribute to improved clinical care for the patient and to organizational efficiency itself. An understanding of how the individual and contextual elements of stroke are related to each other gives us a broad, ambitious view of this scenario, now supported by a structural model that provides empirical evidence, in contrast to the formerly fragmented or nonexistent evidence in prior contributions to our understanding of this disease.

#### Methodological Contributions

Contributions from this type of analysis of large clinicaladministrative databases are obvious. First, this approach goes beyond the classic, correlational methodology that establishes covariation relationships between study variables but has many limitations with respect to establishing causal relationships. In fact, certain prior studies have shown that when empirical models are based on associations between variables, and an SEM model is later developed, some of the previous association relations are not sustained in the new structural model, because of accumulated measurement errors. Second, while carrying out prior inferential analyses ensures that interdependence (or causal) relationships between variables are consistent, this type of analysis is unable to present such relationships in a combined, multidirectional manner, but only as limited to each multivariate analysis. Third, the SEM model makes it possible to establish structural multi-directionality of causation through path analysis. Consequently, this type of analysis would be appropriate to an R&D&I Department (47) within the hospital context, where it would be possible to test the efficiency of hospital interventions and healthcare resources (48, 49).

#### Limitations and Prospects

A first methodological limitation is that no latent variables have been defined in the model. Latent variables can establish a generic relation between constructs, but not the specific ones that we wanted to find. In our case, we have tried to define the causal relationships between observable variables. From our point of view, this precise relationship is very important.

The data are taken from non-lysed strokes. The clinical situation today is a different one (intravenous fibrinolysis and mechanical thrombectomy), where the role of the stroke unit is even more critical. However, given the high prevalence of this subtype of stroke (ischemic and not subject to reperfusion), we think that establishing a predictive empirical model with personal and hospital variables is of great relevance. It would be interesting to replicate the study with patients who have received treatment for acute stroke, when enough data become available. We believe that the future inclusion of patients subjected to mechanical or chemical reperfusion would probably modify the outputs in the sense of less sequential morbidity, decreased length of stay, and lower mortality in stroke units and even in general hospitalization. We also consider that the contextual dependent variable "readmissions" would be favorably diminished by the inclusion of these new therapeutic techniques. Even in the case of nonlysed stroke, this replication in a real cohort would make it possible to simplify and further divide up the elements of the final model. We could learn more precisely which elements might be implemented in routine clinical care in order to optimize outcomes.

Working with these massive clinical-administrative databases has the advantage of the great statistical power of a large sample size, but such databases are not free from significant drawbacks. On one hand, the data reflect the in-hospital situation exclusively, possibly leading to an external validity issue; in our particular case, acute stroke patients are rarely addressed on an outpatient basis, so we consider this bias to be minimal. We also must consider that the information is limited by the quality of the diagnostic and procedural codings themselves, and that this quality is rather uneven, not only geographically (different healthcare regions) but also over time (the study period), fortunately the latter tends toward improvement.

In addition, we must consider the limitation that variables such as "epilepsy" imply, where we cannot identify whether it is occurring as a result of the stroke or whether the patient has suffered this pathology for some time. This obvious database limitation in not differentiating certain secondary diagnoses as complications or as comorbidity is only partially compensated by the high sample size and the diagnostic position: epilepsy encoded in the second diagnostic position is understood to be an acute complication, while in lower positions it is more likely to be a preexisting comorbidity.

Finally, we must take into account the very critical patients who die shortly after admission: their chronic pathologies are often under recorded, possibly distorting the statistical results and even provoking paradoxical results. The wellknown Jencks bias, a phenomenon described in Jencks et al. (50), has been confirmed in multiple studies. Studies by Dahlin et al. (51) are most noteworthy, where under recording was proven to be a constant, even when as many as 25 diagnoses had been reported upon discharge. For all these reasons, such biases in the information source are difficult to control, but given the sample size, power and level of detail, this source provides extremely valuable information for patient care and for improved organizational management.

# AUTHOR CONTRIBUTIONS

JdlF development of the conceptual idea. Statistical methodology Global drafting of the manuscript. JG-T general review of the manuscript. Introduction and review-writing of the discussion. MI-E general review of the manuscript and partial wording of the discussion. GS global review of the manuscript. Statistical review. AG-U review of the design evaluation process used. JF-P review of the global English level of the manuscript.

#### ACKNOWLEDGMENTS

The authors thank to Daiichi-Sankio Company for the financial support for publication expenses as well as the encouragement to complete the project. The mentioned company had no role in the

#### REFERENCES

design of the study, collection, analysis, interpretation of data and in writing the manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur. 2019.00498/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 de la Fuente, García-Torrecillas, Solinas, Iglesias-Espinosa, Garzón-Umerenkova and Fiz-Pérez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Trajectories of (Bio)markers During the Development of Cognitive Frailty in the Doetinchem Cohort Study

M. Liset Rietman1,2 \*, Gerben Hulsegge3 , Astrid C. J. Nooyens 1 , Martijn E. T. Dollé1,4 , H. Susan J. Picavet 1 , Stephan J. L. Bakker 5 , Ron T. Gansevoort 5 , Annemieke M. W. Spijkerman1 and W. M. Monique Verschuren1,2

1 National Institute for Public Health and the Environment, Bilthoven, Netherlands, 2 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands, 3 Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 4 Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States, 5 Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, Netherlands

#### Edited by:

Ding Ding, Fudan University, China

#### Reviewed by:

Jorge Matias-Guiu, Complutense University of Madrid, Spain David Della-Morte, University of Miami, United States Giancarlo Logroscino, University of Bari Aldo Moro, Italy

> \*Correspondence: M. Liset Rietman<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neuroepidemiology, a section of the journal Frontiers in Neurology

Received: 19 October 2018 Accepted: 24 April 2019 Published: 31 May 2019

#### Citation:

Rietman ML, Hulsegge G, Nooyens ACJ, Dollé MET, Picavet HSJ, Bakker SJL, Gansevoort RT, Spijkerman AMW and Verschuren WMM (2019) Trajectories of (Bio)markers During the Development of Cognitive Frailty in the Doetinchem Cohort Study. Front. Neurol. 10:497. doi: 10.3389/fneur.2019.00497 Background: Long-term changes in (bio)markers for cognitive frailty are not well characterized. Therefore, our aim is to explore (bio)marker trajectories in adults who became cognitively frail compared to age- and sex-matched controls who did not become cognitively frail over a 15 year follow-up. We hypothesize that those who become cognitively frail have more unfavorable trajectories of (bio)markers compared to controls.

Methods: The Doetinchem Cohort Study is a longitudinal population-based study that started in 1987–1991 in men and women aged 20–59 years, with follow-up examinations every 5 years. For the current analyses, we used data of 17 potentially relevant (bio)markers (e.g., body mass index (BMI), urea) from rounds 2 to 5 (1993–2012). A global cognitive functioning score (based on memory, speed, and flexibility) was calculated for each round and transformed into education and examination round-adjusted z-scores. The z-score that corresponded to the 10th percentile in round 5 (z-score = −0.77) was applied as cut-off point for incident cognitive frailty in rounds 2–5. In total, 455 incident cognitively frail cases were identified retrospectively and were compared with 910 age- and sex-matched controls. Trajectories up to 15 years before and 10 years after incident cognitive frailty were analyzed using generalized estimating equations with stratification for sex and adjustment for age and, if appropriate, medication use. Results were further adjusted for level of education, depressive symptoms, BMI, and lifestyle factors.

Results: In men, (bio)marker trajectories did not differ as they ran parallel and the difference in levels was not statistically significant between those who became cognitively frail compared to controls. In women, total cholesterol trajectories first increased and thereafter decreased in cognitively frail women and steadily increased in controls, gamma-glutamyltransferase trajectories were more or less stable in cognitively frail women and increased in controls, and urea trajectories

**116**

increased in cognitively frail women and remained more or less stable in controls. Results were similar after additional adjustment for potential confounders.

Conclusions: Out of the 17 (bio)markers included in this explorative study, differential trajectories for three biomarkers were observed in women. We do not yet consider any of the studied (bio)markers as promising biomarkers for cognitive frailty.

Keywords: cognitive frailty, markers, biomarkers, trajectories, Doetinchem Cohort Study

# INTRODUCTION

Frailty is a state of increased vulnerability to adverse health outcomes when exposed to stressors caused by the cumulative decline in one or more domains of functioning, including the cognitive domain (1–4). Moderate cognitive decline is part of the normal aging process (5). Some elderly are confronted with accelerated cognitive decline, which could eventually lead to (mild) cognitive impairment or dementia. A (reversible) state of cognitive vulnerability within mild cognitive impairment has been termed "Cognitive frailty" (6). Although the existence of cognitive frailty and its definition are still under debate (7), there seems to be broad agreement that cognitively frail people experience accelerated cognitive decline (i.e., cognitive dysfunction) without having a form of dementia (8). In this study, people were considered to be cognitively frail when their global cognitive functioning was poor, given their level of education.

It is not yet fully understood how cognitive frailty develops and whether it can be detected at an early stage. However, there are indications that processes of inflammation and oxidative stress are involved and that C-reactive protein (CRP) could potentially serve as a biomarker (9). In addition, in previous studies we observed associations between body mass index (BMI), self-reported health, several biomarkers (e.g., βcryptoxanthin and zeaxanthin), and cognitive frailty (10, 11). Unfavorable changes in these and other (bio)marker levels may precede cognitive frailty. Studying these changes can provide insight into the molecular pathways involved and could point out promising biomarkers for cognitive frailty.

In the Doetinchem Cohort Study (DCS), various markers (e.g., self-reported health, BMI) and biomarkers (e.g., CRP, urea), have been measured over a time span of at least 15 years (12). Out of these (bio)markers, we identified 17 possibly relevant (bio)markers for cognitive frailty. These are mainly cardiometabolic, inflammatory, and oxidative stress markers. These types of markers have been linked to cognitive decline (13) and could therefore possibly serve as biomarkers for cognitive frailty. Since cognitive frailty arises gradually, it is meaningful to study how (bio)markers "behave" in the course of developing cognitive frailty. These insights can be helpful for the development of treatment and prevention. Therefore, our aim is to explore the trajectories of several (bio)markers during the development of cognitive frailty in adults and compare these to the trajectories of age- and sex-matched controls. We hypothesize that those who become cognitively frail have more unfavorable trajectories of (bio)markers compared to controls.

# METHODS

### Cohort

The DCS is a longitudinal population-based cohort study starting in 1987–1991 (round 1) examining 7,769 men and women aged 20–59 years living in Doetinchem, a town in the Netherlands. Adults who participated in the first round were invited for followup examinations in 1993–1997 (round 2, n = 6,117, mean age: 46 years), 1998–2002 (round 3, n = 4,918, mean age: 51 years), 2003–2007 (round 4, n = 4,520, mean age: 56 years), and 2008– 2012 (round 5, n = 4,018, mean age: 60 years). Response rates were 75% or higher in all rounds. Verschuren et al. (14) and Picavet et al. (12) have described the study design in more detail. All participants gave written informed consent in each round and the study was approved by the Medical Ethics Committee of the University Medical Center Utrecht.

#### Incident Cognitive Frailty

Cognitive functioning was assessed among participants aged ≥45 years. Trained personnel carried out the cognitive tests according to a standardized protocol. In rounds 2–5, global cognitive functioning was measured with a neuropsychological test battery assessing three domains: memory function, information processing speed and cognitive flexibility. These were tested using the 15 Words Verbal Learning Test (VLT) (immediate and delayed recall) (15), the Stroop Color–Word Test (16), the Word Fluency Test (17), and the Letter Digit Substitution Test (18). Nooyens et al. (19) have described the cognitive tests in more detail. From the separate test scores, one global cognitive functioning score was calculated for each round. Next, the global cognitive functioning scores were transformed into z-scores, based on the mean and standard deviation in round 5, and were adjusted for level of education and for the number of tests performed during follow-up to take a possible learning effect into account. The z-score that corresponded to the 10th percentile in round 5 (z-score = −0.77) was applied as cut-off point for incident cognitive frailty in rounds 2, 3, 4, and 5. This is consistent with the definition used in one of our previous studies, where we also defined people as being cognitively frail when their global cognitive functioning was poor, given their level of education (11). Since the prevalence of frailty naturally increases with age, this was not included in the definition of cognitive frailty. Participants with a score below the cut-off point were considered incident cognitively frail and participants with a score above this value were considered not cognitively frail. As cognitive tests were only performed among participants aged ≥45 years, we defined participants <45 years as not being cognitively frail. Participants aged ≥45 years without data on cognitive functioning were excluded.

#### Measurements

#### Markers

Weight and height (to calculate the BMI), waist circumference, and diastolic and systolic blood pressure were measured according to standard protocols (14). Standardized questionnaires were used to obtain data on self-reported health, depressive symptoms (assessed with the Mental Health Inventory-5 and the Vitality dimension of the 36-Item Short-Form Health Survey) (20, 21), level of education, smoking status, alcohol consumption, physical activity (categorized using the Cambridge Physical Activity Index) (22), use of anti-hypertensive medication, cholesterol-lowering medication, and glucose-lowering medication.

#### Biomarkers

Total and high-density lipoprotein (HDL) cholesterol were measured with standardized enzymatic methods. In 2013–2014, standardized enzymatic methods were used to retrospectively determine triglycerides, alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), high sensitivity CRP, albumin, uric acid, cystatin C, and creatinine of rounds 2–5 using blood plasma that had been stored in freezers. Participants with only one measurement of the (bio)markers were excluded and participants who were pregnant in a particular round were excluded for that round only. Details of all measurements are described in the **Appendix A** (Supplementary Material).

### Statistical Analyses

#### Matching

The time of incident cognitive frailty was the first examination round in which participants scored below the cut-off point of −0.77. From this point in time, we were able to investigate trajectories with a maximum of 15 years before and 10 years after incident cognitive frailty. There were not enough observations to study the trajectories up to 15 years after incident cognitive frailty. Both cognitive frailty and biomarker levels vary strongly by sex and age. Therefore, we used a matching design in which each incident cognitively frail person was matched to two controls based on sex, age (with 5-year age categories), and examination round. We excluded two incident cognitively frail cases, as no suitable controls could be identified.

#### (Bio)marker Trajectories

Trajectories of (bio)markers for incident cognitively frail cases and controls were analyzed using generalized estimating equations (GEE) with an unstructured correlation structure. The GEE analysis was performed for each (bio)marker (dependent variable) separately and with cognitive frailty ("yes" vs. "no") as the main determinant. This resulted in separate estimates (i.e., adjusted marginal means) at each point in time for cases and controls. With these estimates, trajectories for the cognitively frail and the controls were constructed. This approach is consistent with the method used by Hulsegge et al. (23).

Analyses were stratified for sex and the model included age (linear and quadratic), examination round (categorical variable with round 5 as reference category), and time (categorical variable). Time consisted of six categories ranging from T−15 to T+10 with T0 as the moment of incident cognitive frailty. Age was centered at 60 years because this was approximately the mean age at round 5, which resulted in (bio)marker levels of someone who was hypothetically 60 years old. Trajectories of systolic and diastolic blood pressure were adjusted for self-reported anti-hypertensive medication, trajectories of total cholesterol, HDL cholesterol and triglycerides were adjusted for self-reported cholesterol-lowering medication, and trajectories of glucose were adjusted for the self-reported use of glucoselowering medication.

Triglycerides, ALT, GGT, and CRP had a skewed distribution. Therefore, we log-transformed these biomarkers and reported back-transformed geometric means. Differences between trajectories of cognitively frail people and their controls were tested using an overall interaction term between cognitive frailty and time, and a p-value lower than 0.1 was considered statistically significant. This was obtained via the joint tests for GEE, where differences in slopes were calculated based on five interaction terms (frailty∗T−15, frailty∗T−10, frailty∗T−5, frailty∗T+5, frailty∗T+10) with T0 (i.e., moment of incident cognitive frailty) as reference category.

To summarize, trajectories up to 15 years before and 10 years after incident cognitive frailty were analyzed and compared to controls using GEE with stratification for sex and adjustment for age and, if appropriate, medication use (model 1). We verified whether the results changed after additional adjustment for level of education, depressive symptoms (model 2), BMI, smoking, alcohol intake, and physical activity (model 3). Trajectories of BMI and waist circumference were not adjusted for BMI.

We performed a sensitivity analysis to study the potential impact of loss to follow-up due to mortality on our results. To this end, we excluded participants who died during the followup period (1993–2012) along with their matched case and/or control(s) and compared the results to those obtained in the total population. All analyses were performed using SAS 9.4 software (SAS Institute, Cary, North Caroline, USA).

# RESULTS

# Population Characteristics

After excluding participants ≥45 years without data on cognitive functioning and applying the additional exclusion criteria, 5,139 participants remained for further analyses. Over the course of the study, 6 participants were defined as incident cognitively frail in round 2, 116 participants in round 3, 134 participants in round 4, and 202 participants in round 5. In total, 455 participants became cognitively frail of which 303 (67%) were men and 152 (33%) were women. At incident cognitive frailty (T0), men had an average age of 65.5 (SD 7.6) and women of 66.9 (SD 7.8). Cognitively frail people more often had a low level of education and a slightly higher BMI, waist circumference, and systolic blood pressure than controls (**Table 1**). In addition, cognitively frail TABLE 1 | Population characteristics for incident cognitively frail men and women and their controls at T0.

BMI, body mass index; HDL cholesterol, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; GGT, gamma glutamyltransferase; CRP, C-reactive protein.

men and women more often reported to have poor or fair health and their medication use was higher compared to controls.

# (Bio)marker Trajectories

In our main model (i.e., model 1), we observed no differences in (bio)marker trajectories between incident cognitively frail men and controls as the trajectories ran parallel and the difference in levels was not statistically significant. In women, we observed differences in the shape of the trajectories of total cholesterol (p = 0.067), GGT (p = 0.008), and urea (p = 0.002) between incident cognitively frail women and controls (**Figure 1** and **Appendix B Table 1** in Supplementary Material). Total cholesterol increased before women became cognitively frail and decreased after incident cognitive frailty (T0), while in controls, total cholesterol levels steadily increased over time. GGT was more or less stable in incident cognitively frail women, while in controls, GGT levels slowly increased from T−10 onwards. Urea increased over time in incident cognitively frail women, while in controls, urea levels remained more or less stable.

After further adjustment for level of education and depressive symptoms (model 2), we found no differences in (bio)marker trajectories for men. In women, we found differences in the same biomarker trajectories as in model 1 (i.e., total cholesterol, GGT, urea) and additionally observed a difference in trajectories for ALT in women (p = 0.046) (**Appendix B Table 1** in Supplementary Material). When further adjusting for BMI and life-style factors (model 3), we still observed no differences in (bio)marker trajectories for men. In women, differences in the trajectories for GGT, urea, and ALT between incident cognitively frail women and controls remained, but the difference in total cholesterol trajectory (p = 0.109) was just above our threshold (p-value for interaction <0.1).

FIGURE 1 | Trajectories of (bio)markers for incident cognitively frail men and women and their controls. Trajectories of self-reported health (A), body mass index (B), waist circumference (C), diastolic blood pressure (D), systolic blood pressure (E), total cholesterol (F), HDL cholesterol (G), glucose (H), triglycerides (I), ALT (J), GGT (K), CRP (L), albumin (M), cystatin C (N), creatinine (O), uric acid (P), and urea (Q) of incident cognitively frail people (red lines) and controls (green lines) with 95% confidence intervals stratified by sex and corrected for age and, if appropriate, medication use (model 1), where men and women were hypothetically 60 years old at the time of incident cognitive frailty. A difference (p-value for interaction <0.1) in (bio)marker trajectory between those with and without incident cognitive frailty are indicated by an asterisk. HDL cholesterol, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; GGT, gamma glutamyltransferase; CRP, C-reactive protein. Geometric means are shown for triglycerides, ALT, GGT, and CRP.

To explore the potential impact of loss to follow-up due to mortality on our results, we performed a sensitivity analysis using model 1 in which we excluded participants who died (n = 130) during the follow-up period (1993–2012). In men, 61 cognitively frail and 40 controls died. In women, 16 cognitively frail and 13 controls died. Excluding these participants, along with their matched case and/or control(s), resulted in the exclusion of 333 participants in total. After exclusion, in men, the trajectories of total cholesterol (p = 0.061) and BMI (p = 0.082) differed between cognitively frail men and controls. In women, consistent with the observed differences in the total population, the trajectories of total cholesterol (p = 0.066), GGT (p = 0.018), and urea (p = 0.001) differed between cognitively frail women and controls. In addition, the trajectories of ALT (p = 0.057), and albumin (p = 0.057) also became different between cognitively frail women and controls (**Appendix B Figure 1** in Supplementary Material).

# DISCUSSION

Our aim was to examine whether (bio)marker trajectories differ for those who become cognitively frail compared to those who do not over a follow-up of 15 years. In addition, we hypothesized more unfavorable trajectories for those who became cognitively frail compared to controls. In our main model (model 1), we observed no differences in (bio)marker trajectories between incident cognitively frail men and controls as the trajectories ran parallel and the difference in levels was not statistically significant. In women, we observed differences in the shape of the trajectories of total cholesterol, GGT, and urea between incident cognitively frail women and controls.

Against our expectations, most of the 17 (bio)markers included in this study did not show deviating trajectories between those who became cognitively frail and those who did not. This was, for example, surprising for the inflammation marker CRP, since this biomarker has, longitudinally, been linked to physical frailty (24), cognitive decline (25), and risk of dementia (26, 27), and therefore could serve as a biomarker for cognitive frailty. However, also other epidemiological studies report inconsistent findings regarding longitudinal measures of inflammation markers and cognitive decline (28, 29). In addition, Soysal et al. (30) showed that higher CRP and interleukin-6 (IL-6) levels are cross-sectionally associated to physical frailty, but not longitudinally. Although this study focused on physical frailty, it seems consistent with our null findings regarding cognitive frailty.

We did find a difference in the trajectory for total cholesterol in women. Total cholesterol increased before women became cognitively frail and decreased after incident cognitive frailty (T0), while in controls, total cholesterol levels steadily increased over time. In a previous cross-sectional study in MARK-AGE, we also found lower cholesterol levels among people who were already cognitively frail compared to people without frailty (10). Solomon et al. (31) found that non-demented people with high total cholesterol levels around age 50 had poorer cognition 20 years later. In addition, their total cholesterol levels decreased after age 50. The pattern Solomon et al. (31) describe seems comparable to the trajectory we found in incident cognitively frail women. The decline we observed in total cholesterol levels after becoming cognitively frail could be caused by various factors, one of which is medication use. The analyses were adjusted for selfreported use of cholesterol-lowering medication, but information about medication type, dose and therapy compliance were not collected and could therefore not be included in these analyses. Hence, it is unclear whether the trajectories differ due to the occurrence of cognitive frailty, or whether other effects, like a treatment effect, is underlying this difference.

We also observed different urea and GGT trajectories in incident cognitively frail women compared to controls. Higher urea levels can be caused by disrupted blood flow through the kidneys for example through heart failure (32). In contrast, we found lower GGT levels in incident cognitively frail women, while heart failure would also cause increased, and not decreased, GGT levels (33). Lower GGT levels could be caused, for example, by the use of clofibrate, a lipid-lowering agent controlling high cholesterol and triglyceride levels. When adjusting for the use of cholesterol-lowering medication for GGT, results remained similar. It is suggested that serum GGT within the normal range is an early marker for oxidative stress (34). Oxidative stress has been suggested to be associated with frailty (35) and increased GGT levels in later life (80 years and older) were associated with cognitive decline (36). However, we unexpectedly observed lower instead of higher GGT levels in cognitively frail women compared to controls, indicating that cognitive frail women might have less oxidative stress. Overall, there does not seem to be a reasonable explanation for the course of these trajectories. We cannot exclude the possibility that the trajectories of GGT and urea are chance findings.

Differences in (bio)marker trajectories were observed among women, but not among men. We found that more men had poor cognitive functioning compared to women (i.e., more men were identified as incident cognitively frail than women). On the other hand, differences in cognitive functioning between the incident cognitively frail men and their controls were smaller compared to the differences observed in women. Possibly, the women who we identified as incident cognitively frail were relatively worse off than the incident cognitively frail men, having relatively poorer cognitive function and potentially poorer overall health and therefore we only found differences between trajectories for women.

We studied the potential impact of loss to follow-up due to mortality on our results. This was appropriate since some of the trajectories for women indicated a (rapidly) deteriorating health. For example, decreasing cholesterol levels could be caused by malnutrition and increasing urea levels could be caused by heart failure. As expected, mortality rate was higher among those who became cognitively frail compared to those who did not. However, excluding these participants did not materially change the results. In fact, the differences in trajectories for total cholesterol, GGT, and urea in women remained the same and seemed therefore rather robust.

We considered people as being cognitively frail when their global cognitive functioning was poor, given their level of education. Since the prevalence of frailty naturally increases with age, this was not included in the definition of cognitive frailty. The most important difference between our operationalization of cognitive frailty and the classic operationalization for mild cognitive impairment (MCI) (37) is that our definition of cognitive frailty did not include subjective memory complaints. Also, we did not include self-reported activities of daily living which is part of the MCI definition. Recently, a definition for cognitive frailty was proposed combining physical frailty with MCI (38). Since we have previously observed that it is possible to be cognitively frail without being physically frail (10), we defined cognitive frailty only based on cognitive functioning. In our manuscript, the term "cognitive frailty" represents cognitive dysfunction, independent of other (physical) limitations and is only based on poor cognitive functioning given the level of education. We explicitly adjusted for level of education, since highly educated people can also experience cognitive dysfunction and this would otherwise be masked by their cognitive reserves.

One of the strengths of this study is that both cognitive functioning, using a comprehensive neuropsychological test battery, and multiple (bio)markers were objectively measured in four rounds over a follow-up of 15 years, making this a unique cohort for studying the relation between (bio)markers and cognitive functioning over time. In addition, all biomarkers of rounds 2–5 were measured in a single run, limiting interassay variation.

This study has some limitations. We tried to include all relevant confounders in the analyses (model 3) but residual confounding may still be present. However, adjustment for confounders had a marginal effect in the results. Further, cognitive functioning was not assessed in participants younger than 45 years. We defined these participants as not being cognitively frail under the assumption that people become cognitively frail with advancing age, mostly from 60 years onwards. In addition, since this population was still relatively young, it could have been too young to find (bio)markers for cognitive frailty. Moreover, since cognitive frailty can be described as a complex syndrome, multiple factors can influence the development of this syndrome. This makes it challenging to identify biomarkers for cognitive frailty. Further, we were unable to exclude participants with dementia. However, given the age-distribution of the cohort, the prevalence and incidence of dementia would be quite low and is therefore unlikely to have influenced our results. Finally, due to the age- and sex-matched

#### REFERENCES

study design, the power was limited to analyze differences at any time point. However, more power would probably not lead to other differences in trajectories, because most trajectories run almost parallel and that aspect is unlikely to change with more power.

In conclusion, out of the 17 (bio)markers included in this explorative study, different trajectories between incident cognitively frail women and their controls were found for three biomarkers. However, the relation between these biomarkers and the development of cognitive frailty is unclear. Future studies are needed to confirm these findings. Given the results of this study, we do not yet consider any of the studied (bio)markers as promising (bio)markers for cognitive frailty.

#### ETHICS STATEMENT

The study was approved by the Medical Ethics Committee of the University Medical Center Utrecht.

### AUTHOR CONTRIBUTIONS

All authors were involved in interpreting the data, drafting, and approving the manuscript.

### FUNDING

This work was supported by the Ministry of Health, Welfare and Sport of the Netherlands, The National Institute for Public Health and the Environment (grant number S132002) and by Biobanking and Biomolecular Resources Research Infrastructure-NL (grant number CP2011-27).

# ACKNOWLEDGMENTS

We would like to thank the epidemiologists and fieldworkers of the Municipal Health Service in Doetinchem for their contribution to the data collection for this study.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur. 2019.00497/full#supplementary-material

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Rietman, Hulsegge, Nooyens, Dollé, Picavet, Bakker, Gansevoort, Spijkerman and Verschuren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Rate and Determinants of Recurrence at 1 Year and 5 Years After Stroke in a Low-Income Population in Rural China

Jing Han1†, Wenjing Mao1,2†, Jingxian Ni 1,3,4, Yanan Wu1,3,4, Jie Liu1,3,4, Lingling Bai 5 , Min Shi 1,3,4, Jun Tu1,3,4, Xianjia Ning1,3,4 \* and Jinghua Wang1,3,4 \*

*1 Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China, 2 Department of Neurology, The Affiliated Hospital of North China University of Science and Technology, Tangshan, China, 3 Laboratory of Epidemiology, Tianjin Neurological Institute, Tianjin, China, 4 Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China, 5 Department of Neurology, Liaocheng People's Hospital, Liaocheng, China*

#### Edited by:

*Ding Ding, Fudan University, China*

#### Reviewed by:

*Shing-Jong Lin, National Yang-Ming University, Taiwan Cui Mei, Fudan University, China*

#### \*Correspondence:

*Xianjia Ning<EMAIL_ADDRESS>Jinghua Wang<EMAIL_ADDRESS>
*†These authors have contributed equally to this work and share first authorship*

#### Specialty section:

*This article was submitted to Neuroepidemiology, a section of the journal Frontiers in Neurology*

Received: *11 December 2018* Accepted: *02 January 2020* Published: *23 January 2020*

#### Citation:

*Han J, Mao W, Ni J, Wu Y, Liu J, Bai L, Shi M, Tu J, Ning X and Wang J (2020) Rate and Determinants of Recurrence at 1 Year and 5 Years After Stroke in a Low-Income Population in Rural China. Front. Neurol. 11:2. doi: 10.3389/fneur.2020.00002* Recurrent stroke is becoming an increasingly important public health issue owing to the increased risk of disability and death. However, population-based studies investigating the rate of recurrent stroke in China are rare. We explored the rate and determinants of recurrent stroke within 1 and 5 years after the initial stroke in a rural population in China. Data for stroke events were obtained from the Tianjin Brain Study, conducted between 1992 and 2016. The age-standardized rates of recurrent stroke within the first year and the first 5 years after the initial stroke were calculated for this period. Determinants of recurrent stroke were assessed using Cox regression analyses. The overall age-standardized rate of recurrent stroke within 1 year was 5.7% (men, 6.9%; women, 4.6%); within 5 years, the overall recurrent stroke rate was 22.5% (men, 24.0%; women, 20.2%). The recurrence rate increased with advancing age and decreased with increased educational attainment. Age ≥65 years and a history of alcohol consumption were independent risk factors for recurrent stroke within 1 year after the incident stroke, after adjusting for age, sex, education, hypertension, diabetes, smoking, and alcohol consumption. However, the risk of recurrent stroke within 5 years after the incident stroke was positively associated with male sex, age ≥65 years, a lower level of education, known diabetes, and alcohol consumption, after adjusting for the previously indicated covariates. These findings suggest a crucial need to address risk factor management among stroke patients to reduce the burden of stroke, especially among low-income populations. Furthermore, a multicenter, large sample, nationwide study is urgently needed.

Keywords: stroke, recurrence, predictors, epidemiology, incidence

# INTRODUCTION

Stroke is one of the leading causes of death and disability, worldwide (1) and is responsible for ∼4.4 million deaths (9% of total) annually (2). Over the past two decades, there has been a notable increase (84%) in the absolute numbers of stroke survivors globally although the incidence of stroke has decreased in high-income

**124**

countries (3). However, the number of stroke survivors under 75 years old was almost 30% higher in low- and middle-income countries than in high-income countries (3). The risk of recurrent stroke is becoming increasingly important owing to its associated high risk of disability and death (4–9), and the risk varies according to the elapsed time since the incident stroke (10–15). The population-based cumulative risk of recurrent stroke during the 5 year period after the incident stroke has been reported to be 19% in Manhattan (USA), 29% in Rochester (USA), 30% in Oxfordshire (UK), and 32% in Perth (Australia) (16–19).

Each year, there are ∼2.5 million new stroke cases reported in China, and currently there are ∼7.5 million stroke survivors; nationally, the stroke recurrence rate remains high (11.2%) (20). Over the past few decades, the incidence of stroke has increased in China both in urban and rural populations (21–25). Thus, recurrent stroke prevention is of considerable importance to both individuals and overall public health. However, few populationbased studies have described the incidence of recurrent stroke in China, especially among rural populations. Therefore, in this study, we aimed to assess the rate of recurrent stroke within 1 and 5 years after the incident stroke in rural China.

# METHODS

#### Study Population

This study was a population-based stroke surveillance study that began in 1985 in Tianjin, China. The study design has been previously described (23–25). Briefly, it included 15,438 residents living in a township in Tianjin, China, where 95% of the adults were low-income farmers. The primary source of income was grain production, and the annual per capita income was <\$100 US in 1990 and <\$2000 USD in 2015 (26). In 1991, the illiteracy rates among residents 35–74 years old were 30% for men and 40% for women. The population characteristics remained stable over the study period (27). Since neuroimaging technology became available in 1992, this study analyzed recurrent stroke events since that time.

The study protocol was approved by the ethics committee of Tianjin Medical University General Hospital (TMUGH); written informed consent was obtained from each participant.

# Stroke Definition and Types

All included stroke events were symptomatic and diagnosed using pre-established criteria, including clinical features and imaging evidence (MRI or CT scan). The initial (incident) stroke was defined as the first occurrence of rapidly developing signs of focal neurologic disturbances of presumed (no other apparent cause) vascular etiology lasting >24 h (28). Transient ischemic attacks (TIAs) and silent strokes (diagnosed by imaging only) were excluded, and stroke cases with TIA histories were regarded as incident events. Patients with transient symptoms and concurrent neuroimaging evidence of brain infarctions were considered to be stroke cases, based on the "tissue" definition (29). Recurrent stroke was defined as a new stroke event occurring at least 28 days after the incident event.

The stroke types included ischemic stroke (IS), intracerebral hemorrhage (ICH), and unknown. IS was defined as a thrombotic brain infarction, cardioembolic stroke, or lacunar infarct.

#### Event Ascertainment Processes

All incident stroke events included first-ever and recurrent strokes registered according to the previously published procedure (25). Stroke event determinations were performed using a predetermined procedure. First, local village physicians reported stroke events (both initial and recurrent) to the community hospital within 24 h of onset. Second, community hospital physicians then visited the surviving patients' homes to confirm stroke events and obtain clinical feature information within 72 h. Confirmed stroke events, diagnosed using imaging modalities, were reported each month to neurologists at TMUGH. Suspected events (no imaging performed) were reported in a timely manner. Finally, the TMUGH neurologist identified possible stroke events via door-to-door interviews as soon as possible. To ensure that all incident stroke events were registered, three sources were used to obtain information regarding stroke events. First, local physicians reported events according to a predefined procedure. This was the main information resource, as the local physicians were the first medical professionals to contact the patients. Second, in cases involving inpatient stroke events, the medical records were obtained from the hospital or insurance company. Third, information from the all-cause death registry supplemented stroke events missing from the registry.

Information regarding stroke onset was obtained during interviews conducted by the community hospital physicians and the TMUGH neurologist; information included demographics, time of stroke onset, clinical signs, and previous stroke status. Other information, including imaging characteristics, prescribed therapy, and post-discharge outcomes, was obtained by the TMUGH neurologist during interviews with survivors, their family members, or a local health worker.

The recruitment period was from January 1, 1992, to December 31, 2016, and follow-up was completed on December 31, 2017. During the surveillance period, all stroke events and all-cause deaths were registered and followed. The dates of death or emigration from the township were determined using population registries. All changes in demographic information were recorded, including births, deaths, immigrations (including that due to marriages), and emigrations (including that due to entering a high school or university or working in the city). Peasant workers were included in this study because all residents working in the city were seasonal workers who regularly returned to the township during traditional festivals and the farming season.

#### Inclusion and Exclusion Criteria

Only patients who survived the index event were entered into the present analysis. Death was considered to have been caused by the incident stroke if it occurred within 28 days of symptom onset. All patients with at least 1 year of follow-up after their first-ever stroke events were included in this study. Follow-up evaluations were conducted every month, and recurrent strokes were classified using information from interviews, conducted directly during follow-up visits or by telephone calls to patients, next of kin, witnesses, or the attending physicians.

All patients with TIAs; suspected stroke deaths without imaging evidence or confirmation by a TMUGH neurologist; and silent strokes, detected only by imaging, were excluded from this study. Patients suffering neuroimaging-confirmed strokes underwent computed tomography or magnetic resonance imaging examinations in the county central hospital.

#### Statistical Methods

Categorical variables are presented as numbers (%), and continuous variables are presented as means (standard deviations, SD). Patient ages were categorized into 3 subgroups (<45, 45–64, and ≥65 years) when the demographic features of this population were described and the rates of recurrent stroke were assessed; however, only 2 subgroups (<65 and ≥65 years) were used to assess the determinants of recurrent stroke. Education attainment was categorized into 3 subgroups (0, 1–6, and ≥7 years). Smoking status was categorized into 3 subgroups (never smoker, past smoker, and current smoker); similarly, drinking status was also categorized into 3 subgroups (never drinkers, past drinkers, and current drinkers). The recurrent stroke incidence was calculated as the cumulative frequency of recurrent events at 1 and 5 years after the incident stroke. Association of education attainment with the risk of the recurrent stroke was assessed by Kaplan-Meier survival analysis and presented as log rank.

Multivariable Cox regression analyses were used to identify the predictors of recurrent stroke within both time periods after adjusted by covariates (sex, age, education attainment, stroke types, previous history of hypertension and diabetes, smoking and drinking status); the risk of recurrent stroke occurrence is presented as adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). The follow-up time was recorded in years and was calculated as the interval between the date of the initial stroke and the date of the recurrent stroke for those patients experiencing recurrent strokes within 1 and 5 years after the initial stroke. For those patients without recurrent strokes within 1 or 5 years after the initial stroke, the follow-up time was defined as 1 or 5 years, respectively. Moreover, for patients who died within 28 days after the initial stroke, the follow-up time was defined as the interval between the initial stroke and the date of death. During the study period, stroke patients who experienced initial strokes before immigrating to the township were included in the study; stroke patients who emigrated from the township after the initial stroke were excluded from the study. However, in this study, no stroke patients immigrated to or emigrated from the township during the study period. SPSS version 15.0 for Windows (SPSS, Chicago, IL, USA) was used for the analyses; statistical significance was defined as P < 0.05.

#### RESULTS

#### Patient Characteristics

A total of 1,185 individuals experienced initial stroke events, including ICH, IS, and undefined events between 1992 and 2016. All 1,185 patients were included in the determination of the rate and associated predictors of recurrent stroke 1 year after the incident event; patients experiencing incident strokes between January 1, 1992, and December 31, 2012 (n = 899), were included in the determination of the 5 year recurrent stroke rate and its associated predictors.

Between 1992 and 2016, 1,185 initial stroke events were included in this study; the mean time to the first recurrent stroke within the first year was 0.82 years; for the 5 year time frame, the mean time to recurrence was 3.08 years. In both study periods, patients experiencing initial strokes were more likely to be men, older, and less educated. Further, IS was the most common stroke event during these periods (**Table 1**).

TABLE 1 | Descriptive characteristics of patients with first-ever stroke by time period in this study.

*SD indicated standard deviation; 95% CI indicates 95% confidence interval. ICH, Intracerebral hemorrhage; IS, Ischemic stroke; UND, Undefined stroke.*

#### Recurrent Stroke Rate

During the study period, 71 recurrent strokes occurred in the 1 year follow-up group and 202 occurred in the 5 year follow-up group. The overall rate of recurrent stroke within 1 year was 5.7% (men, 6.9%; women, 4.6%); in the 5 year group, the overall rate of recurrent stroke was 22.5% (men, 24.0%; women, 20.2%).

**Table 2** shows an increasing rate of recurrent stroke that corresponds with increasing patient age at the time of the initial stroke onset; compared with patients aged <65 years old, the rate of recurrent stroke among patients ≥65 years old was higher (7.4%, P = 0.037) in the 1 year follow-up group.

There was no significant association between the rate of recurrent stroke within 1 year and education attainment (log rank = 1.103; P = 0.576; **Figure 1A**). However, there was a negative association between the rate of recurrent stroke within 5 years and education attainment (log rank = 8.462; P = 0.015; **Figure 1B**).

#### Determinants of Stroke Recurrence

Compared with patients aged <65 years old, the risk of stroke recurrence within 1 year after the initial stroke more than doubled among patients ≥65 years (P = 0.004) after adjusting for age, sex, education level, hypertension, diabetes, smoking status, and drinking status; there was a lower risk of recurrence for those experiencing an IS than for those with an initial ICH. The risk of recurrent stroke within 5 years after the incident stroke was 1.65-fold higher among men than among women (P = 0.017). Compared with patients aged <65 years, the risk of recurrence in patients ≥65 years old increased 1.54-fold (P = 0.009). Similarly, the risk of recurrence in patients with <7 years of education was almost double that of patients with at least 7 years of education (P = 0.017). The risk was also 1.71-fold higher among those with a previous diabetes diagnosis than among those without diabetes (P = 0.008), and the risk of recurrence was more than two-fold higher among patients who ever drank compared to those who never drank (P = 0.008; **Table 3**).

# Determinants of Stroke Recurrence Stratified by Age

**Table 3** shows the determinants of recurrent stroke after stratifying the population by age. A history of diabetes was the most common risk factor for recurrent stroke within 1 and 5 years after the initial stroke among patients aged <65 years; the recurrence risk increased 2-fold compared to patients without diabetes. Moreover, the risk of recurrence within 5 years was 1.9 fold higher among those with 1–6 years of education compared to those with more than 6 years of education (P = 0.028).

Among patients aged ≥65 years, the risk of recurrence within the first post-stroke year was 57% lower in patients experiencing initial IS than in those experiencing initial ICH (P = 0.033); this patient group also had a 79% lower risk of recurrent stroke among those who ever smoked than among those who had never smoked (P = 0.046). However, there was a 3.6-fold higher risk of recurrence for those who ever drank than among those who never drank (P = 0.025). The risk of recurrence within 5 years increased >2-fold among patients who ever drank compared to that among those who never drank (P = 0.021).

# DISCUSSION

This is the first population-based report evaluating the recurrent stroke rate in rural China. In this study, we assessed the rate of recurrent stroke within 1 (5.7%) and 5 (22.5%) years after the first-ever stroke. The recurrence rate over both time periods increased significantly with advancing age. After adjusting for age, sex, education level, hypertension, diabetes, smoking, and alcohol consumption, advanced age was an independent predictor of recurrent stroke within both time frames among the overall population. Moreover, male patients, patients with lower educational attainment, and those with a known diabetes history had higher risks of recurrent stroke within the 5 year period. Further, age-stratified analysis showed that known diabetes history was an independent risk factor of stroke recurrence at both 1 and 5 years after the initial stroke among stroke patients

aged <65 years old. Moreover, patients with a lower educational attainment (1–6 years) had a high risk of stroke recurrence. However, for patients aged 65 years and over, ever drinking was an independent risk factor of recurrent stroke at both 1 and 5 years after initial stroke onset. In addition, experiencing initial IS and ever smoking were independent protective factors for risk of stroke recurrence among elderly individuals.

and education attainment (log rank = 8.462; *P* = 0.015; B).

The stroke recurrence rate remains controversial. The risk of stroke recurrence is reported to range from 7.0 to 20.6% over the first year (4, 12, 13), from 16.2 to 35.3% over the first 5 years (4, 10, 14), and from 14 to 51.3% over the first 10 years after the initial stroke (4, 10, 15). A population-based cohort study from the Middle East indicated that the cumulative incidence of stroke recurrence was 14.5% by the end of 5 years, with the highest rate occurring during the 1st year after the initial stroke (5.6%) (29). In the present study, the 1 and 5 year recurrence rates were 5.7 and 22.5%, which are within the range reported previously. This finding suggests that more than 20% of patients will suffer a recurrent stroke within the first 5 years. The relatively low recurrence rates observed in this study are most likely due to the relatively young (average age <65 years) study population.

The disparity among the reported recurrence rates may be partially explained by the differing definitions of stroke recurrence. There is wide variation in the definition of recurrent stroke, ranging from any focal neurological deficit lasting >24 h and occurring after an initial stroke (14, 16, 18, 30) to an event occurring >28 days after an initial stroke (31, 32). In the present study, recurrent stroke was defined as a stroke occurring >28 days after the incident stroke.

In the present study, age was identified as a predictor of recurrent stroke. A previous study from northern Sweden indicated that the risk of stroke recurrence rose by 3% for every additional year of age (8). A similar trend was reported in a study from the Middle East where a 2% increase in risk was observed for every additional year of age (29). Another study suggested that the risk of recurrence significantly increased among patients >75 years old compared to patients <65 years old; the risk of recurrence increased by 13% in patients 76–85 years old and by 16% in those >85 years old (33). In the present study, relative to patients who were <65 years old, we observed a 129% and a 54% increase in the 1 and 5 year risks, respectively, of recurrent stroke among patients ≥65 years old.

Reports of sex-based differences in stroke outcomes are also inconsistent. Several previous studies have reported worse outcomes among women than among men, with women demonstrating greater functional impairment, mortality, recurrence, and dependency rates 3 and 12 months post-stroke (1, 34, 35). However, other studies have not reported these sexbased differences (36). In the present study, there was a higher risk of recurrence among men than among women within the 5 year period after the incident stroke, but this difference was not evident within the first year. In this population, the young age of stroke onset and the poor management of post-stroke risk factors among men may have contributed to the observed sex-based differences in recurrence.

There is strong evidence for an inverse relationship between socioeconomic status (SES) and stroke incidence and mortality (37–39). However, evidence for a potential association between SES and stroke recurrence is limited (39). Previous studies

\**P* < *0.05 compared to reference.*

suggested that this relationship was sex-based (40, 41), but no significant association between SES and stroke recurrence was found in another population-based-study (10). In the present study, a higher risk of recurrent stroke within 5 years after the incident stroke was observed in patients with lower levels of education (1–6 years) than in those with higher levels of education (≥7 years) in the overall population and in those aged <65 years old. However, the risk of recurrent stroke within 5 years after the incident stroke was not higher in those without education attainment (illiteracy) in the overall population and among those aged <65 years old; the lower number of patients with higher levels of education (≥7 years) in the overall population (11.3%) and the lower number of patients with lower levels of education among those aged <65 years old (15%) may partly explain this disparity. Furthermore, the higher percentage of female patients in the illiterate group may contribute to the association of lower education levels with poor stroke outcomes. Poor risk factor management and limited access to medicine among those with lower educational attainment and income may partially explain this relationship (42, 43).

The association between smoking and stroke risk remains controversial. In the Framingham Heart Study, smoking cessation was associated with a significantly lower risk of cardiovascular disease within 5 years among heavy smokers relative to current smokers; however, relative to never smokers, former smokers' cardiovascular disease risk remained significantly elevated beyond 5 years after smoking cessation (44). Another study from the Intracerebral Hemorrhage Outcomes Project found that the functional outcomes among patients with intracerebral hemorrhage were similar between recent smokers and former smokers (45). In the present study, a lower risk of recurrent stroke was found in patients who had ever smoked in the overall population and among elderly stroke patients within 1 year after their initial stroke. The neuroprotective effect of nicotine might explain this finding partly (46–48). The exact cause however needs further research.

Heavy alcohol consumption is well-known risk factor for stroke (49, 50), but the relationship between the risk of recurrent stroke and alcohol consumption remains unknown, especially in China. Alcohol consumption independently predicted impairment 2 years after stroke in one study (51), but no association with functional outcomes after stroke was found in other studies (52–54). In this study, ever drinking was an independent risk factor for recurrence in the overall population and among elderly stroke patients at both 1 and 5 years after the initial stroke. In this population, patients drank in the past ceased drinking alcohol after developing severe diseases; this may explain the positive association between ever drinking and stroke recurrence.

Several studies have suggested that diabetes mellitus is an important predictor of recurrent stroke (55–57). Consistent with these studies, in the present study, we observed a significant association between diabetes mellitus and stroke recurrence for the whole study population and those patients aged <65 years.

This study has several limitations. First, the study population was from a township in northern China, which is not representative of the overall population of China. However, the prospective study design and long study period may have decreased recall and selection bias. Moreover, the 362,596 person-years of total follow-up fulfill the 100,000 person-years of follow-up criterion for population studies (58). Second, we did not collect detailed information regarding dietary habits and medication use; therefore, other possible determinants of stroke recurrence could not be assessed in this study. Third, data on smoking and alcohol consumption were not available in this study; this may affect the specific evaluation of the relationship between smoking or alcohol consumption and stroke recurrence. Fourth, our study lacks data on medication usage. However, in this low-income population, the rate of using medicine was lower; thus, the impact of medication usage on stroke recurrence may be ignored. Finally, information regarding medical care among these stroke patients was not available. This was a low-income, poorly educated population, and few had medical insurance before 2008. Thus, they would have been taken care of by family members, including spouses, children, siblings, and others. Only unmarried men who suffered strokes were sent to the official nursing home.

#### CONCLUSIONS

This report assessed the rate of recurrent stroke in rural China using a population-based study design. The rates of recurrent stroke within 1 and 5 years after the first-ever stroke were 5.7 and 22.5% and increased significantly with advancing age. The determinants of stroke recurrence were associated with age. After adjusting for covariates, a known diabetes history was an independent risk factor for stroke recurrence at both 1 and 5 years after the initial stroke among patients aged <65 years old; moreover, lower educational attainment (1–6 years) increased the risk of stroke recurrence. However, for patients aged 65 years and over, ever drinking was an independent risk factor for recurrent stroke at both 1 and 5 years after the initial stroke

## REFERENCES

onset; experiencing an initial IS and ever smoking were protective factors for the risk of stroke recurrence. These findings suggest a crucial need to address risk factor management among stroke patients to reduce the burden of stroke, especially among lowincome populations. Furthermore, a multicenter, large sample, nationwide study is urgently needed.

# DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the artical/supplementary material.

# ETHICS STATEMENT

The study protocol was approved by the ethics committee of Tianjin Medical University General Hospital (TMUGH); written informed consent was obtained from each participant.

# AUTHOR CONTRIBUTIONS

JW and XN contributed to the study design, performed data collection, data interpretation, and critical review. JW performed data analysis. JH and WM contributed to drafting of the article. JH, WM, JN, YW, JL, LB, MS, and JT performed data collection, case diagnoses, and confirmation of case diagnoses. All authors read, revised, and approved the final version of the paper.

## ACKNOWLEDGMENTS

We thank all participants of the Tianjin Brain Study, and local medical care professionals for their valuable contributions.

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Han, Mao, Ni, Wu, Liu, Bai, Shi, Tu, Ning and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


**About the Editor**

#### **Christian N. Madu**

Christian N. Madu, PhD is Professor of Management Science at the Lubin School of Business, Pace University, New York, and Professor of Environmental Management and Control at the University of Nigeria. His research interests are environmental planning and applications of management techniques to solve sustainability issues. Prof. Dr. Christian N. Madu is on the editorial board of several journals and has also served as editor-in-chief of major international journals. He has published widely in multidisciplinary fields and is the author/editor of more than 17 books by leading academic publishers. His current interests are chemical safety and security management.


*Article* **Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence**

**Davide Facchinetti 1,\*, Stefano Santoro 1,\*, Lavinia Eleonora Galli 1, Giulio Fontana 2, Lorenzo Fedeli 3, Simone Parisi 3, Luigi Bono Bonacchi 3, Stefan Šušnjar 3, Fabio Salvai 4, Gabriele Coppola 4, Matteo Matteucci 2 and Domenico Pessina 1**

**Abstract:** Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named "rover"), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover traveling speed were studied. To assess the effectiveness of the spraying coverage, for each run several water-sensitive papers were placed throughout the vegetation. Compared to the commonly distributed mixture volume (1000 L/ha), the prototype is able to reduce up to 55% of product sprayed, but still assure an excellent crop coverage.

**Keywords:** greenhouse; precision spraying; rover; environment protection; operator's health; consumer's safety


**5. Advantages of radiofrequency energy**

Radiofrequency (RF) is a form of electromagnetic energy that can be converted to thermal energy. It does not target a particular chromophore. The early RF devices created bulk heating. When thermal energy emanated from the device tip, adjacent tissue heating occurred, leading to gradual warming and uneven heating, which became problematic and unpredictable in large treatment surface areas. With gradients of heat, areas of fibrosis can result from excessive heat delivery, which introduces further potential issues in regions of pre-existing compromised blood flow, little adiposity, and thin skin. Multiple RF devices, monopolar (Thermi) and bipolar (Invasix, InMode) with internal and external temperature probes and indwelling liposuction aspiration cannulas, were introduced in the past 12 years. With encouraging results, and safety features that monitor treatment depth and skin temperatures to help avoid visible burns, or the safety to offer rapid cooling to avoid burns altogether, the use of RF devices for enhancing body contouring have gained traction [11, 12]. In addition, other aesthetic applications have emerged to shrink lax soft tissue, as for SMAS tightening during rhytidectomy and popcorn capsulorraphy in breast implant repositioning surgery, and ongoing efforts to manage abdominal wall fascial laxity [13]. As described in the next section, a novel RF based device, Renuvion® (Apyx™ Medical), uses an energized stream of helium gas, known as a plasma, for the same purpose of precise soft tissue subdermal coagulation.

Like the lasers, the thermal energy delivered by an RF device can be calculated and provide treatment guidelines. The amount of energy is the product of the square of current multiplied by tissue impedance multiplied by the time of application [5, 14]. As adipose tissue has high tissue resistance or impedance to electromagnetic current, it has proven to be a good target for RF technologies to diminish iatrogenic tissue laxity and enhance liposuction results. Recalling that the total ability to re-drape the skin is related to the contractile nature of the dermis, as reflected by dermal thickness, the amount of FSN and fat to be aspirated, the delivery of RF energy to multiple tissue planes has proven advantageous.  The delivery of immediate heating between 60 and 80°C

*Contractile Effects of Radiofrequency Energized Helium Plasma on the Fibrous Septal Network DOI: http://dx.doi.org/10.5772/intechopen.97849*

causes collagen fibrils to contract to one third their length and alters the microenvironment of the extracellular matrix toward favorable remodeling and healing. This begs the question as to whether repeated thermal effects on dermal ground substance can occur, and if there is a role for retreatment in secondary surgery to gain additional contraction. Notwithstanding, significant longevity of skin contraction has been demonstrated in clinical bipolar RF assisted liposuction studies that have shown 25% soft tissue area contraction at 6 months and 35–40% at one year [18]. As radiofrequency offers immediate and long- lasting thermal contraction of the FSN and inflammatory dermal heating, it has come to the forefront of contemporary energy assisted liposuction technologies, like Renuvion®.