Influence of Climatic Factors on the Abundance and Profusion of Mosquitoes in Eastern Province, Saudi Arabia

*Assad Al-Thukair, Yasin Jemal and Alexis Nzila*

### **Abstract**

This study was performed to evaluate the change in seasonal abundance and distribution of individual mosquito vectors (Culex, Anopheles, and Aedes) in relation to the climatic factors in Eastern Province, Saudi Arabia, for the study period of 2014. The association between mosquito abundance and environmental parameters was investigated using bivariate and multivariate analysis. The study showed the range of temperature and relative humidity required for individual mosquito larvae abundance varies for Culex, Anopheles, and Aedes. However, no variation was observed in the range of temperature and relative humidity required for the abundance of adult Culex and Anopheles. The results revealed a negative relationship between mosquito larval/adult abundance and temperature (Total number of larva/adult is 671/11 in July, While it is 2462/221 in January). There is a link between relative humidity and rainfall, as the three climatic factors together were responsible for 33.1% (R2 = 0.331), 54.6% (R2 = 0.546), and 86.6% (R2 = 0.866) of the variance on Culex, Anopheles, and Aedes larvae, respectively. The effects of the three climatic parameters of temperature, relative humidity, and rainfall on mosquito larval and adult abundance were discussed. In addition, influences of other environmental factors on larval/adult mosquito distribution and abundance were also explained.

**Keywords:** mosquitoes, climate factors, Saudi Arabia


**3. Clotting cascade: factor Xa function**

Factor X or Stuart-Prower Factor named after the male patient named Stuart in 1957 and a female patient named Prower with FX deficiency [45, 46]. Moreover, Factor X has long been known to have a key role in hemostasis and plays a central part in the blood-clotting cascade by catalyzing the production of thrombin, which leads to clot formation and wound closure [33, 47].

An ideal anticoagulant would prevent thrombosis without inducing systemic hypocoagulation, and would thereby prevent undesired bleeding complications. Thus, a factor Xa inhibitor could potentially have the properties of a desirable anticoagulant. In the search for new drugs, anticoagulant serine protease activated factor Xa is a particularly promising target and has attracted a strong interest in the last 5 years.

As the discovery of small-molecule protease inhibitors has progressed, this convention has been amplified to denote drug substructures that similarly bind to substrate amino acids

FXa Direct Synthetic Inhibitors

19

http://dx.doi.org/10.5772/intechopen.76518

In the 1980s, early attempts to identify FXa inhibitors were prompted by prior thrombin inhibitor discoveries such as the compounds illustrated in **Figure 7** (**a** and **b**), which are examples of early [61] and most recent direct FXa inhibitors rivaroxaban (**Figure 7c**) and apixaban

Development of rivaroxaban was a major breakthrough in anticoagulation drug discovery and was the first approved orally active direct FXa inhibitor. However, recently studies have shown that rivaroxaban and apixaban discontinuation could result in thromboembolic events, and the use of rivaroxaban associated with warfarin increases the risk of major bleeding in non-valvular atrial fibrillation patients [62–64]. Through the study of the chemical structures of these inhibitors illustrated below, it also became evident that various substitutes could be

In spite of extensive knowledge about the clotting mechanism, its complexity poses a considerable challenge to the research and development of powerful anticoagulants that are both

As it was exposed before, FXa plays a critical role in coagulation. Together with FVa and calcium ions on a phospholipid surface, FXa forms the prothrombinase complex, which is responsible for the conversion of prothrombin to thrombin, the final effector of coagulation

Oral anticoagulant drug discovery efforts initially focused on the development of smallmolecule anticoagulants that target thrombin directly—the oral DTIs. But, there is some evidence to suggest that inhibition earlier in the coagulation cascade at the level of FXa may have greater antithrombotic potential. In addition preclinical studies suggest that FXa inhibitors may possess a wider therapeutic index than DTIs. Thus, it is understandable that a great number of pharmaceutical companies dedicated to the discovery of this oral anticoagulant drug have finally and determinedly concentrated on small-molecule, direct FXa inhibitors

It is worth considering briefly some of the significant molecular characteristics of the target protein. FXa belongs to the family of serine proteases such as trypsin; the catalytic domain consists of two antiparallel β-barrel folds that together form the catalytic triad and the substrate binding site. Accordingly to the Schetcher and Berger nomenclature each protein subsite (Si) binds the amino acid (Pi) residue [79]. Specifically, FXa is composed by four principal

S1 is an anionic pocket—hydrophobic and deep cleft—formed by Tyr228, Ser195, and Asp189; and S4 subsite has three domains to link with the ligand: one hydrophobic pocket defined by Tyr99, Trp215, and Phe174, one cationic hole formed by Glu97 and Lys96, and a water pocket where the natural substrate is trapped under the following amino acids: Thr98, Ile175, and

(**Figure 6**) [54].

safe and effective.

(**Figure 1**).

[33, 56, 57, 74–78].

subsites S1, S2, S3, and S4.

(**Figure 7d**) chemical structures.

**3.1. FXa structural target points**

accepted in both the S1 and S4 regions [45, 65–73].

FXa plays an important role in first and secondary hemostasis. It produces the core catalyzing reaction that results in thrombin enzyme formation by means of the blood coagulation cascade which results in clot formation and wound closure [33, 47]. Moreover, FXa was found to play a central role in the coagulation process leading to hemostasis in the original extrinsic/intrinsic model [33] as well as in the newly proposed cell-based model. Factor X can be activated through either the intrinsic or extrinsic pathway. Initiation of both pathways activates the inactive precursor FX to FXa. Considering that one molecule of FXa catalyzes the formation of 1000 thrombin molecules, this amplification step can be substantial. Moreover, both pathways lead to the propagation and amplification of coagulation through the activation of FX.

The perfect antithrombotic agent would not induce systemic hypocoagulation and thus provides equilibrium between clot formation and secondary problems such as bleeding. The investigation into finding new anticoagulant agents reveals that serine protease FXa is an important validated pharmaceutical achievement whose use has grown remarkably since the beginning of the twenty-first century [42–44]. Thus, an FXa inhibitor combined with an antiplatelet moiety could possibly provide the features of an effective drug, thus preventing the platelet aggregation during the hemostasis process, avoiding the thrombus formation and inhibiting the catalyzing FXa reaction [33, 48–53].

As explained above, FXa performs a crucial function in the coagulation process. Thus, FXa provides a specific target for novel anticoagulant agents. The synthesis of direct FXa inhibitors that are able to effectively inhibit prothrombinase-associated and clot-bound FXa, and therefore provide greater potential anticoagulant activity, is therefore a significantly important advance. There is enough evidence to imply that inhibition earlier during primary hemostasis in the coagulation cascade at the FXa level could provide higher antithrombotic potential by using inhibition of platelet adhesion drugs. Furthermore, preclinical studies indicate that FXa inhibitors possibly possess a broader therapeutic index. Therefore, there is a significant number of pharmaceutical companies, which are working to discover new anticoagulant drugs, and have finally decided to focus on small molecules such as direct FXa inhibitors [54–59].

FXa is a serine protease which catalyzes the production of 1000 thrombin molecules involving the interaction on the platelets surface, Ca2+ ions, and FVa called the prothrombinase complex. The prothrombinase complex acts on the natural substrate producing the catalytic coagulation process.

Structurally FXa, like trypsin, belongs among the family of serine proteases within the catalytic domain, which is formed by two antiparallel β-barrel folds that act in tandem to produce the catalytic triad and the substrate binding site. Schechter and Berger (**Figure 6**) have provided a nomenclature adopted by scientists, which describes the prototypical binding site of a serine protease. Consequently, each protein subsite, labeled Si, binds its related amino acid substrate, labeled Pi [60].

As the discovery of small-molecule protease inhibitors has progressed, this convention has been amplified to denote drug substructures that similarly bind to substrate amino acids (**Figure 6**) [54].

In the 1980s, early attempts to identify FXa inhibitors were prompted by prior thrombin inhibitor discoveries such as the compounds illustrated in **Figure 7** (**a** and **b**), which are examples of early [61] and most recent direct FXa inhibitors rivaroxaban (**Figure 7c**) and apixaban (**Figure 7d**) chemical structures.

Development of rivaroxaban was a major breakthrough in anticoagulation drug discovery and was the first approved orally active direct FXa inhibitor. However, recently studies have shown that rivaroxaban and apixaban discontinuation could result in thromboembolic events, and the use of rivaroxaban associated with warfarin increases the risk of major bleeding in non-valvular atrial fibrillation patients [62–64]. Through the study of the chemical structures of these inhibitors illustrated below, it also became evident that various substitutes could be accepted in both the S1 and S4 regions [45, 65–73].

In spite of extensive knowledge about the clotting mechanism, its complexity poses a considerable challenge to the research and development of powerful anticoagulants that are both safe and effective.


**Author details**

Andrea Lechiancole\*, Massimo Maiani, Igor Vendramin, Sandro Sponga and Ugolino Livi Cardiothoracic Department, University Hospital of Udine, Italy

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**CELLULOSE - FUNDAMENTAL ASPECTS AND CURRENT TRENDS**

Edited by **Matheus Poletto** and **Heitor Luiz Ornaghi Júnior**

#### **Cellulose - Fundamental Aspects and Current Trends**

http://dx.doi.org/10.5772/59889 Edited by Matheus Poletto and Heitor Luiz Ornaghi Junior

#### **Contributors**

Gunnar Westman, Mikaela Borjesson, J. Vincent Edwards, John Rojas, Tamilselvan Mohan, Karin Stana Kleinschek, Silvo Hribernik, Rupert Kargl, Guillermo Riva, Jose A. Silva, Fernando Lopez-Dellamary, Maria E. Hernandez, Joaquin García-Estrada, Brenda Vega, Bruno Medronho, Poonam Singh, Hugo Duarte, Filipe Antunes, Nicolas Le Moigne, Jan Dormanns, Benoît Duchemin, Mark Staiger, Luis Alves, Nevin Çankaya, Roosevelt Delano, Paulo Ronaldo, Ana Siqueira, Carla Eiras, Josy Anteveli, Edson Cavalcanti, Josy Osajima, Fabricia Silva, Luciano Lima, Hernane Da Silva Barud, Wilton R Lustri, Helida G Oliveira Barud, Maristela Fs Peres, Junkal Gutierrez, Agnieszka Tercjak, Osmir B Oliveira Junior, Sidney Jl Ribeiro

#### **© The Editor(s) and the Author(s) 2015**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2015 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Cellulose - Fundamental Aspects and Current Trends Edited by Matheus Poletto and Heitor Luiz Ornaghi Junior

p. cm. ISBN 978-953-51-2229-6 eBook (PDF) ISBN 978-953-51-5059-6


**2. Tourism carrying capacity: A review of concept**

The concept of TCC, though emerged in the 1970s and 1980, has received significant attention in recent years as part of an effective strategy to address environmental, economic, and social issues [6–8].

There have been many attempts to define carrying capacity. It was stated that TCC is a specific type of environmental carrying capacity and refers to the biophysical and social capacity of the environment with respect to touristic activity and its development [9–10]. Middleton and Hawkins (1998) [11] defined carrying capacity as "a measure of the tolerance a site or building are open to tourists activity and limit beyond which an area may suffer from the adverse impacts of tourism." TCC can also be defined as "the maximum number of people that use tourism site without unacceptable effect on environmental resources while meeting the demand" [12].

Chamberlin (1997) [13] defines it as "the level of human activity an area can accommodate without the area deteriorating, the resident community being adversely affected or the quality of visitors experience declining." Clark [14] defines carrying capacity as a "certain threshold level of tourism activity beyond which there will occur damage to the environment, including natural habitats." He also states that the "actual carrying capacity limit in terms of numbers of visitors or any other quota or parameter is usually a judgement call based upon the level of change that can be accepted, regarding sustainability of resources, satisfaction of resource users, and socio-economic impact" [14]. The TCC represents "the maximum level of visitor use that an area can maintain, that is, the limit of human activity: if this level is exceeded, the resource will deteriorate" [15].

The UN World Tourism Organisation (WTO) defines TCC as "the maximum number of people that may visit a tourist destination at the same time, without causing destruction of the physical, economic, socio-cultural environment and an unacceptable decrease in the quality of visitors' satisfaction." Tourism operations in protected areas need to be planned carefully and monitor regularly to ensure their long-term sustainability. Otherwise, such operations will have negative consequences and tourism will contribute to the further deterioration of these areas. Many of the protected areas have promoted tourism for their social, economic, and livelihood opportunities of the local residents [15–19].

Individual tourist destinations were studied assessing the TCC all over the world [20–24]. It has been suggested that development of a tourist destination should be based on their innate capacities for tourism. TCC was considered as an appropriate tool for management and remains one of the most useful and applied techniques for tourism and recreation planning, if combined with other management tools [25, 26].

Coccossis and Mexa [6] showed that carrying capacity assessment remains a powerful concept that can be used for planning and management of sustainable tourism. Many studies calculated carrying capacity using physical, ecological, psychological, and economical approach. Because of expanding degree of environmental threat with growing tourism, a suitable method needs to be carried out and carrying capacity stays one of the applied and effective methods [27]. Hamed and Fataei [28] estimates the TCC to Fandoqloo forest in Iran using physical, real, and effective carrying capacities. Results of their study showed that each tourist spot has its specific priorities and the carrying capacity of each region differs according to the environmental conditions. Nghi et al. [23] assessed the environmental carrying capacities for Phong Nha-Ke Bang and Dong Hoi using three basic components: ecological, economic, and social. The authors have calculated the TCC in Dong Hoi and Phong Nha centers by using the adjustment from physical carrying capacity (PCC) to real carrying capacity (RCC) based on various limiting factors such as infrastructure and management capacities. Their results show that Dong Hoi center has the highest TCC and Phong Nha has a lower TCC than other centers in Quang Binh.

Lagmoj et al. [29] in their study evaluated TCC in three ways viz., PCC, RCC, and ECC, and found that "ECC is in low range due to lack of required facilities and infrastructures as well as manpower for management and providing tourism services for tourists." They estimated the "PCC in Khorma forest as 3712 persons/day while RCC, considering limiting factors including the number of very hot days and the number of wet days, is 2001 persons/day. The ECC, taking the management capabilities including the number of manpower and the budget, 69 persons/day was calculated for Khorma forest". Armin and Calichi [30] in their study of PCC, the factors viz., tourist flows, the size of the area, the optimum space available for each tourist and the visiting time, were considered. Their results showed that the total visitors to the park in 2012 were 220 visitors per day that actual average park visitation are higher than the estimated carrying capacity and stated that the expanding recreational use would affect production and other forest benefits in the long term and suggested that a recreational forest use plan must be adjusted by the planner to keep the park more exploiting.


4.1.5. Composite Score (CS)

Composite score (CS) is selected because it comprehensively evaluates the fiscal regimes of oil and gas projects. The linear weighting method is used to consider the indicators of GT and FLI. Thus, the linear weighting function of the composite score is built as

$$\text{CS} = \text{W1} \times \text{GT} + \text{W2} \times \text{FLI}\_{\prime} \tag{19}$$

where W1 and W2 reflect the attitude of the investor relating to the contribution of GT and FLI to the attractiveness of the fiscal regimes. This is determined by the decision-makers in accordance with their own experience. The CS is a comprehensive indicator that reflects the attractiveness of fiscal regimes. The interpretation for the CS is that the smaller the CS, the greater the attractiveness of the fiscal terms in the petroleum contract.


**Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study**

**Nina Wawro 1,2,\*, Giulia Pestoni 1,3, Anna Riedl 1, Taylor A. Breuninger 1,2, Annette Peters 4,5, Wolfgang Rathmann 5,6, Wolfgang Koenig 7,8,9, Cornelia Huth 4,5, Christa Meisinger 1,2, Sabine Rohrmann 3 and Jakob Linseisen 1,2**

Received: 8 May 2020; Accepted: 3 June 2020; Published: 5 June 2020

**Abstract:** There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (*p* < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39–0.90 for AHEI and OR = 0.60, 95% CI = 0.40–0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.

**Keywords:** diabetes mellitus; dietary pattern; metabotype; metabolic phenotype; Mediterranean Diet Score; Alternate Healthy Eating Index

#### **1. Introduction**

During the past decades, the prevalence of type-2 diabetes mellitus (T2DM) has rapidly and dramatically increased [1]. This poses a major public health problem, especially in Western societies. It is estimated that the number of diabetic adults worldwide will increase by 50% to a total of 693 million by 2045 [2]. According to the International Diabetes Federation, about 50% of T2DM cases are undetected [2]. These numbers clearly highlight the ever-increasing severity of the diabetes epidemic and the pressing need to investigate its underlying pathophysiology so that better therapeutic strategies can be developed.

Prediabetes and diabetes mellitus are associated with an increased risk of complications, namely cardiovascular disease (CVD), retinopathy, neuropathy, and nephropathy [3]. Besides a lack of sufficient physical activity, a major role in the development of diabetes mellitus is attributed to diet [4,5]. A change in lifestyle can reduce the relative risk of developing diabetes mellitus by 40–70% in prediabetic persons [6]. Thus, the thorough investigation of dietary habits and their effects on the development of chronic diseases can have a major impact on public health.

In traditional approaches, single food groups or nutrients are examined with respect to their associations with certain diseases. Yet in reality, food groups and nutrients are not consumed in isolation. Therefore, nutritional epidemiology nowadays commonly examines dietary patterns. Dietary patterns make it possible to capture a comprehensive picture of a person's overall diet and thereby consider possible interactions between nutrients or food groups [7,8]. A diet quality score is a dietary pattern defined a priori and is a useful tool to measure the overall quality of a diet. Two frequently used diet quality scores are the Alternate Healthy Eating Index (AHEI) [9] and the Mediterranean Diet Score (MDS) [10]. Dietary patterns have been consistently associated with T2DM in various studies investigating different ethnic populations, sexes or age groups [11–22], although metabolic characteristics have so far not been comprehensively accounted for.

Interindividual differences in metabolism and dietary requirements are driven by intrinsic as well as extrinsic factors, e.g., (epi-)genetic background, body composition, and lifestyle [23–27]. The identification of metabotypes, i.e., metabolically-homogenous subgroups, has been established to adequately incorporate this knowledge when modelling diet-disease associations and in the context of personalized nutrition [28–35]. The metabotype identification consists of comparing individuals' metabolic characteristics to each other and grouping similar individuals together. They are assigned the same metabotype. Selecting which parameters should be included in the process of defining metabotypes offers the possibility to encompass a variety of important biological processes. However, one major limitation of the concept is that there is still no unique definition of the term "metabotype" available, nor a single way to identify homogenous groups. Our group is currently investigating the selection of an optimal set of biochemical markers. A previously-published study by our group showed differential associations between single foods groups and T2DM across different metabotypes, underlining the importance of taking metabotype information into account [33].

The aim of the present study is therefore to examine the associations between dietary patterns and T2DM while accounting for metabotype. This is of special interest when developing targeted nutritional recommendations.

#### **2. Materials and Methods**

#### *2.1. Study Population*

The Cooperative Health Research in the Region of Augsburg (KORA) FF4 study is a cross-sectional, population-based study that was conducted in the Bavarian region of Augsburg in 2013/2014. It is the second follow-up of the KORA S4 survey that took place from 1999 to 2001. Details on S4 as well as FF4 have been published previously [36,37]. Briefly, 2279 individuals, the vast majority with Caucasian ethnicity, participated in the KORA FF4 study. They were physically examined, answered self-administered questionnaires, and participated in computer-assisted face-to-face interviews during their study center visit. Fasting blood samples were taken at the study center and immediately underwent preprocessing. For our analyses, we excluded participants without dietary information (*n* = 677, leaving 1602), without metabotype information (*n* = 41, leaving 1561), with type 1 diabetes (*n* = 3, leaving 1558), unclear glucose tolerance status due to missing oral glucose tolerance test (OGTT) information (*n* = 38, leaving 1520), or unclear/unvalidated glucose tolerance status (*n* = 1, leaving 1519). Furthermore, participants with missing information on their hypertensive status (*n* = 2, leaving 1517), and those with severe diseases, namely myocardial infarction (*n* = 46) and/or stroke (*n* = 34) and/or cancer (*n* = 158) were also excluded. The final data set for the analysis included 1287 participants.

The KORA studies were approved by the Ethics Committee of the Bavarian Chamber of Physicians and carried out in accordance with the Declaration of Helsinki (EK Nr. 06068, 25.10.2012). Written informed consent was obtained from all participants.

#### *2.2. Covariates*

The analyses described below included age (in years) and waist circumference (in cm) as continuous variables, sex (male/female), smoking status (never/former/current), education (< 10 years/10–12 years/> 12 years), physical activity (active/inactive) and actual hypertensive status (yes/no) as categorical covariates. The categorization of the education variable represents the German education system. Physically-active participants are those who were active during summer and winter and at the same time active for at least 1 h per week during at least one season. Hypertensive individuals included those with measured systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or use of anti-hypertensive medication given that the subjects were aware that they had hypertension.

#### *2.3. Metabotypes*

Participants were grouped into three metabolically-homogenous subgroups. Details are given in References [34] and [33]. Briefly, a set of 16 clinical markers (body mass index (BMI), 12 laboratory parameters measured in serum, i.e., glucose, total cholesterol, high-density lipoprotein cholesterol, total cholesterol/high-density lipoprotein cholesterol ratio, low-density lipoprotein cholesterol, uric acid, triglycerides, gamma-glutamyltransferase, glutamate-pyruvate transaminase, glutamate-oxaloacetate transaminase, alkaline phosphatase high-sensitivity C-reactive protein and insulin, and two parameters measured in fresh venous whole EDTA blood, i.e., leukocytes and glycated hemoglobin) was used to identify and characterize three clusters by applying the k-means clustering algorithm. These 16 clinical markers are the available subset of markers originally used in the development of this metabotype concept. The prevalence and incidence of metabolic diseases in the clusters are used to quantify how well the clinical markers define distinct clusters. Missing observations were imputed by MICE (multivariate imputation by chained equations) and values were standardized prior to running the clustering algorithm. Metabotypes were derived in a larger sample of 2218 (out of 2279 total) FF4 participants. Only participants who did not fast for at least 8 h before blood collection (*n* = 54) or participants with more than 10% missing values of all clustering variables (*n* = 7) were excluded. The three cluster solution presented in Reference [33] describes cluster 3 as the most unfavorable metabotype with respect to the metabolic characteristics, e.g., the highest median concentrations of serum glucose and glycated hemoglobin, as well as other risk factors for T2DM, including age, obesity, physical inactivity, family history of diabetes and hypertension. In contrast, clusters 1 and 2 define rather beneficial metabotypes; cluster 1 shows even healthier characteristics than cluster 2 which can be considered as an "intermediate" cluster.

#### *2.4. Dietary Intake and Diet Quality Scores*

Usual dietary intake was calculated based on one food frequency questionnaire (FFQ) and up to three 24 h food lists (24HFL) per participant. One 24HFL was filled in by the participants during their study center visit, and up to two further 24HFLs were filled in at home within the following three months. In contrast to that assessment of the short-term diet, the FFQ assesses the participants' diet over the last 12 months. Details are given elsewhere [38]. Briefly, a two-step approach was used to model consumption probabilities and consumption amounts. Usual intake estimates for the food items were further categorized into food groups based on the EPIC-Soft classification system [39]. Furthermore, estimates of nutrient intake were derived for every participant by linking the usual food group intake to the National Nutrient Database (Bundeslebensmittelschlüssel 3.02).

As measures of quality of the overall diet of the participants, the Alternate Healthy Eating Index (AHEI) 2010 [9] and the Mediterranean Diet Score (MDS) 2003 [10] were calculated based on the usual intake estimates. The AHEI 2010 consists of 11 food components, namely vegetables, fruits, whole grains, sugar-sweetened beverages (SSB) and fruit juice, nuts and legumes, red/processed meat, trans fat, long-chain (n-3) fatty acids, polyunsaturated fatty acids (PUFA), sodium and alcohol. Dietary intake of fish and shellfish was used as a proxy for long-chain (n-3) fatty acid intake. Based on the quantity of each food component consumed, 0 to 10 points were attributed, yielding a theoretical range of 0–110 points for the total AHEI score. The higher the score, the healthier the diet is considered to be. Cut-off values which result in minimum and maximum points per dietary component are given, and intermediate intakes are scored proportionally between those limits. For example, the maximum score of 10 is attributed if at least 5 servings of vegetables per day were consumed. Scoring servings proportionally would, therefore, attribute 2 points for every serving, that is 4 points for 2 servings, 6 points for 3, and so on. These cut-offs have been updated in the 2010 version of the original AHEI [40] according to state-of-the-art practices in nutritional science. In our analysis, the usual intake in g/day had to be converted to servings per day by using standard portion sizes as reported in Reference [9]. For example, one serving of green leafy vegetables equals 1 cup or 236.59 g and for all other vegetables, 1 serving equals 0.5 cup or 118.295 g. Furthermore, we had to exclude the trans-fat component, as this information is not available in our data set. For this reason, the modified AHEI computed in the present study with 10 components ranged theoretically from 0 to 100 points. Table 1 summarizes the components of the modified AHEI and the cut-off values applied in the analysis.

The MDS 2003 includes 9 dietary components, namely vegetables, legumes, fruits and nuts, cereals, fish, meat, dairy products, alcohol, and fat intake (ratio of monounsaturated to saturated fatty acids), where fish has been additionally included to the index originally developed [41]. For each of the components listed above, a value of either 0 or 1 point was assigned to each participant. Except for alcohol intake, sex-specific medians were used as cut-off values. Apart from the components of meat and dairy products, participants with a usual intake above the median for a given component were assigned a score of 1 and those below a score of 0. For the components of meat and dairy products, participants above the median were assigned to a score of 0 and below to a score of 1. Alcohol intakes between 10 and 50 g per day for men and between 5 and 25 g per day for women were considered as optimal, therefore leading to a score of 1 for this component. The total MDS calculated in the present study ranged from 0 to 9 points, where 0 reflects a minimal adherence and 9 reflects a strong adherence to the traditional Mediterranean diet. Table 2 summarizes the sex-specific cut-offs applied in our study.

**Table 1.** Criteria for the definition of the modified Alternate Healthy Eating Index [42].

Adapted from [9]. Abbreviations: SSB: sugar-sweetened beverages; PUFA: polyunsaturated fatty acids.

**Table 2.** Criteria for the definition of the Mediterranean Diet Score [10].

Abbreviations: MUFA: monounsaturated fatty acids, SFA: saturated fatty acids.

#### *2.5. Outcome*

For participants without a diagnosis of diabetes mellitus, glucose tolerance status was determined following the criteria published by the American Diabetes Association [42] based on an OGTT. Criteria that defined normal glucose tolerance status were a fasting glucose concentration of less than 5.6 mmol/L or a 2-h OGTT concentration of less than 7.8 mmol/L. Impaired glucose tolerance (2-h OGTT concentration of 7.8–11.0 mmol/L) and/or impaired fasting glucose (fasting glucose concentration of 5.6–6.9 mmol/L) was classified as prediabetes. Those participants with a fasting glucose concentration of at least 7.0 mmol/L or a 2-h OGTT concentration of at least 11.1 mmol/L were categorized under undetected diabetes mellitus. Participants with prevalent diabetes mellitus were identified by a self-reported diagnosis of T2DM or by the use of anti-diabetic medication. Prevalent cases were validated by contacting the participants' physicians. For the analysis, participants with prevalent and undetected diabetes mellitus were grouped as "T2DM participants" and participants with prediabetes and those with normal glucose tolerance status were grouped as "non-diabetic participants".

#### *2.6. Statistical Analysis*

The characteristics of the KORA FF4 participants are described for the total study population and stratified by metabolically-homogenous clusters. To assess associations between T2DM (dependent variable) and diet quality scores (independent variables), we fitted three different logistic regression models, where the AHEI was scaled by 10 points and the MDS was scaled by 2 points. Model 1 was adjusted for age, sex, and energy intake. Model 2 incorporated additional adjustment for education, physical activity, and smoking. Finally, Model 3 extended Model 2 by including waist circumference and hypertension status. All models were fitted to the total data set as well as stratified by metabotypes. Models fitted to the total data set were presented with and without further adjustment for metabotype. Additionally, for models fitted in the metabotype 1 cluster, smoking status was incorporated as a binary covariate (i.e., never smoker vs. current and former smokers) due to the limited sample size in this cluster. We report odds ratios (OR) and 95% confidence intervals (CI). Additionally, the interaction effects between metabotype and both diet quality scores were assessed by performing Likelihood-Ratio tests comparing the models, including the interaction between metabotype and diet quality scores with the models without interaction.

All statistical analyses were carried out in R software (version 3.5.3 for Windows, R Foundation for Statistical Computing, Vienna, Austria.). The significance level was set to 0.05 for all results.

#### **3. Results**

The characteristics of the study sample are described in Table 3 in total and stratified by the three metabotypes. In the total study sample, there were slightly more women (53.4%) than men and the mean age was 58.3 ± 11.6 years. Subjects in cluster 3 were more often men (64.1%), had the highest mean age (62.0 ± 11.1 years), highest BMI (32.7 ± 5.7 kg/m2), the lowest proportion of current smokers (11.4%) and the highest proportion of prevalent diabetic participants (30.0%) compared to the other two metabotype clusters. Furthermore, metabotype cluster 3 was characterized by the lowest proportion of physically-active participants (43.6%) and the highest proportion of hypertensive participants (65%). Regarding dietary intake, these participants showed the highest mean consumption of meat and meat products (140.1 g/day) and the lowest mean consumption of vegetables (158.1 g/day). Subjects in cluster 1 appeared to be the healthiest and show the highest proportion of normal-weight participants, the highest proportion of physical active status, the lowest proportion of hypertension and the highest proportion of normal glucose tolerance status. Characteristics of participants in cluster 2 were intermediate to cluster 1 and cluster 3. There was little variation in the mean of the two diet quality scores between the metabotype clusters and both measures were in very good accordance, even though they incorporate some different food components. A detailed characterization of the metabotype clusters with respect to the clinical and anthropometric markers for the FF4 study is given in the Supplementary material of Riedl et al. [33].

Results from the logistic regression analyses are presented in Table 4. Without including metabotype information, significant inverse associations between T2DM and both scores were found in Models 1 and 2 for the total study sample. For both diet scores, an additional adjustment for education, physical activity, and smoking led to slightly higher OR estimates that are still below 1. The AHEI predicted a 36% reduction of T2DM odds compared to a 26% reduction predicted by the MDS. When additionally including the metabotype variable as adjusting factor in the total model, a significant inverse association of T2DM and the diet quality scores was only observed when the basic adjustment (age, sex, and energy intake) was applied. Remarkably, a significant interaction effect between diet score and metabotype was found in Model 2 (both scores) and in Model 3 (AHEI). To further examine these interaction effects, we conducted a stratified analysis. In the analysis stratified by metabotype, we found significant inverse diet-diabetes associations in cluster 3 only. It is noteworthy that these associations persisted with further adjustment. A significant 40% reduction of T2DM odds was observed for every 10-point increase in AHEI in Model 2. Likewise, a significant 38% reduction of the odds of diabetes mellitus was observed for every 2-point increase of the MDS in

Model 2. When comparing the different Models to each other, we observed that further adjustment only slightly affected OR estimates. No significant associations with T2DM were seen for the diet quality scores within metabotype cluster 1 or cluster 2.

**Table 3.** Characteristics of the study population overall and by metabotype.

Continuous variables: mean (SD), categorical variables: n (%). BMI categorized following the WHO definition (underweight: BMI < 18.5 kg/m2, normal weight: 18.5 ≤ BMI < 25.0 kg/m2, overweight: 25.0 ≤ BMI < 30 kg/m2, obese: BMI ≥ 30 kg/m2) [43]. BMI: body mass index, SD: standard deviation.

**Table 4.** Associations Between Diet and T2DM in the Total Sample, Stratified by Metabotype.

a: Adjusted for age, sex, energy intake. b: Adjusted for age, sex, energy intake, education, physical activity, smoking. c: Adjusted for age, sex, energy intake, education, physical activity, smoking, waist circumference, hypertension. d: Further adjusted for metabotypes. e: Given the low frequency of current smokers, logistic regressions in cluster 1 were adjusted for smoking status as never vs. former/current, f : Interaction between metabotype and diet quality score. Mediterranean Diet Score (MDS): models per 2 points increase in the MDS score; Alternate Healthy Eating Index (AHEI): models per 10 points increase in the AHEI score. Significant associations are printed in bold.

#### **4. Discussion**

We investigated the association between T2DM and two diet quality scores while taking into account the role of metabotypes in the population-based KORA FF4 study. We found significant interaction effects between metabotype information and both diet quality scores. In the analysis stratified by metabotype, significant inverse associations between T2DM and the two diet quality scores were only detected in the metabolically-unfavorable cluster. We observed a reduction in the odds of T2DM of about 40% for both diet scores in cluster 3. No significant associations were identified within metabotype clusters 1 and 2, i.e., the more metabolically-favorable metabotypes. Without accounting for metabotype information, significant negative associations between T2DM and both quality scores were observed in the total sample. The reduction of the odds for T2DM in the total sample was lower for both diet scores (36% and 24%) in comparison to the estimates derived from the stratified analysis in cluster 3.

When additionally including waist circumference and hypertension as adjustment variables in the logistic regression models, the association between T2DM and diet quality scores was attenuated. This might be explained by the fact that these two factors are potential mediators of diet and T2DM association (e.g., References [16,44–46]). It is known that mediating factors should not be included as adjusting factors in regression models. Therefore, our Model 3 was potentially over-adjusted. The cross-sectional design of the KORA FF4 study does, however, not allow for examining the potential mediation effect of the above-mentioned variables. The inverse association between the diet scores and T2DM was significant after adjustment for energy intake and physical activity. This supports the idea that diet quality is an independent predictor of T2DM status, independent of calorie deficit.

Following a Mediterranean diet has been shown to have protective effects on the development of T2DM by increasing insulin sensitivity and decreasing oxidative stress and inflammation [47]. Anti-inflammatory mechanisms are linked to n-3 fatty acids, which are typically consumed in larger quantities on a Mediterranean diet high in fish, legumes, and nuts [48–51]. Consumption of antioxidants also has positive effects on beta cell function and insulin resistance [52]. The consumption of these dietary components is assessed by the AHEI analogously.

It has been shown that a high-quality diet, as measured by the AHEI and the MDS indices, is inversely associated with the risk for morbidity and mortality of the major chronic diseases, namely CVD, cancer and T2DM [12,14,15,17,53–64]. We examined both indices as they have different features, although they appear similar with respect to the majority of components used. In contrast to fixed cut-off values as used by the AHEI, the MDS applies sex-specific medians of the population that is studied. Therefore, a comparison between different populations or the examination of changes over time is not an appropriate use of the MDS. On the other hand, the MDS approach ensures an equal

contribution of all components to the total score [7]. In our analyses, we obtained similar results for both diet scores. This consistency strengthens our findings.

#### *4.1. A Priori Dietary Patterns and T2DM*

Without including metabotype information in the logistic regression models, we detected significant associations of T2DM and both diet quality scores in the total sample (OR = 0.64, 95% CI = 0.49–0.83 for AHEI, OR = 0.74, 95% CI = 0.57–0.96 for MDS). Several other studies have examined the association between AHEI and T2DM. The results are mostly in line with our findings of significant associations between T2DM and diet quality scores. The Health Professionals Follow-Up Study showed a reduced risk of T2DM with increasing AHEI and increasing alternate MDS (analogous to the MDS but no dairy intake included) in American men [12]. The absolute risk reduction was accentuated among overweight and obese participants [12]. In postmenopausal women aged 50–79 years participating in the Women's Health Initiative, the AHEI and the alternate MDS were inversely associated with T2DM (multivariable-adjusted hazard ratios 0.87 and 0.90 per 1-SD increase in score, respectively) [15]. In the Multiethnic Cohort Study with participants aged 45–75 years, the AHEI and the alternate MDS, among other indices, were examined with respect to their association with T2DM. Both diet quality scores were inversely associated with T2DM [17]. The direction of the associations was mostly consistent across all ethnic groups [17]. The AHEI has been shown to be inversely associated with the risk of having T2DM in an Asian population aged 45–74 years (multivariable-adjusted hazard ratio 0.93 per 1-SD increase in score) [19]. A stratified analysis revealed a significant interaction effect of smoking on the association which was only present in non-smokers (p-interaction 0.03) [19]. This holds for the alternate MDS as well (multivariable-adjusted hazard ratio of 0.93 per 1-SD increase in score).

In American men and women from three prospective cohort studies (Nurses' Health Study (NHS), NHS II and Health Professionals Follow-Up Study) that were analyzed jointly, an improvement of diet and a worsening of diet as assessed by a change in AHEI score have been shown to be associated with a lower and higher risk of T2DM, respectively [16]. Recently, the Atherosclerosis Risk in Communities (ARIC) study did not confirm an association of AHEI and the risk of diabetes mellitus in black and white American men and women aged 45–64 years [60]. However, a significant trend of risk reduction for CVD was observed across AHEI quintiles [60].

Based on 6-year follow-up data from the Multi-Ethnic Study of Atherosclerosis (MESA), no significant association between the MDS and risk of incident diabetes mellitus was found in men and women aged 45–84 years at baseline [11]. In the EPIC-Potsdam Cohort (men and women aged 35–64 years), the MDS was significantly inversely associated with risk of diabetes mellitus (hazard ratio 0.93 per 1 SD increase in score) [18]. Earlier, an analysis of data from 8 EPIC cohorts showed a significant inverse association of the relative MDS (including the same components of the original MDS but using tertiles, not the median) and risk of T2DM [44]. The results of systematic reviews and meta-analyses confirm a relevant risk reduction of diabetes mellitus when following a Mediterranean diet as assessed by the MDS [13,14].

#### *4.2. Inclusion of Metabotype Information to Identify High-Risk Strata*

The inclusion of metabotype information when investigating the association of dietary patterns and T2DM makes our study unique. We only found significant associations between diet quality and T2DM in the cluster representing a metabolically unfavorable metabotype. Numerous cohort studies examined the effect of following certain dietary patterns on the risk of incident T2DM. In most studies, inverse associations were revealed, offering a promising possibility to implement prevention actions for populations based on such dietary patterns. These approaches, however, do not take into account other factors, either intrinsic or extrinsic, that influence the response or non-response to a dietary pattern. Important examples include the composition of gut microbiota and responsiveness to a Nordic diet, as discussed in a review paper [65], the relationship of weight loss and insulin and glucose status [66], or vitamin D responsiveness and markers of metabolic syndrome [67]. Our analysis

included metabotype information as a novel feature when analyzing the association between T2DM and dietary patterns. Metabotypes describing metabolically-similar individuals have previously been suggested [31] and successfully applied in the field of tailored dietary advice [35]. Therefore, we expected to see differences in the associations of T2DM and dietary patterns across the metabotypes. Only in cluster 3, the metabolically least-favorable cluster, were significant inverse associations between T2DM and the diet quality scores present. Prospective studies including metabotype information are needed to further elucidate a possible causal relationship. This could confirm the hypothesis that the protective effect of diet on T2DM is modified by underlying metabotypes, i.e., by metabolic characteristics. This would allow for the identification of metabolically-homogenous subgroups that would benefit the most from the improvement of the quality of the diet as measured by AHEI and MDS.

#### *4.3. Strength and Limitations*

The KORA FF4 study is the second follow-up of KORA S4, a population-based study in the Augsburg study region. The KORA S4 survey was characterized by a high participation rate and was representative of the general Bavarian population. The assessment of glucose tolerance status was carried out by trained and experienced staff and has been validated. The dietary assessment was extensive, and the usual intake was derived by applying a sophisticated method. The estimation of usual intake is based on combining information obtained by long-term and short-term dietary assessment instruments. The application of two diet quality scores and the subsequent results obtained in our analyses strengthen our findings. Nonetheless, the number of prevalent diabetic or unknown diabetic participants is rather small. Due to the limited sample size, some categories for glucose tolerance status had to be combined for the analysis. This resulted in a loss of detailed information. A further limitation of our study is its cross-sectional nature. Our analysis does not allow for drawing causal conclusions about the association of T2DM and a priori dietary patterns. Still, we assume that including undiagnosed diabetic patients in the group of cases strengthens our findings. These participants were not aware of the diagnosis and therefore did not make healthy changes to their dietary behavior, as was probably the case for the already diagnosed participants. Regarding the dietary assessment, we cannot rule out recall bias or over- or under-reporting. One major limitation of the metabotype concept is that there is still no unique definition of the term "metabotype" available, nor a single way to identify homogenous groups, as reviewed in Reference [32] and recently in Reference [68], with a focus on nutrition.

#### **5. Conclusions**

We investigated the role of metabotypes in the association of dietary patterns and T2DM. The inverse association between AHEI and MDS and T2DM was only present among those with an unfavorable metabotype. Prospective studies including metabotype information are needed to confirm these results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.

**Author Contributions:** Conceptualization, J.L.; Data curation, A.P., C.H., C.M. and J.L.; Formal analysis, N.W. and G.P.; Funding acquisition, S.R. and J.L.; Investigation, A.P., W.R., W.K., C.M. and J.L.; Methodology, N.W., G.P., T.A.B. and J.L.; Resources, A.P., W.R., W.K., C.M. and J.L.; Software, G.P. and A.R.; Supervision, S.R. and J.L.; Visualization, N.W. and G.P.; Writing–Original draft, N.W.; Writing–Review & editing, G.P., A.R., T.A.B., A.P., W.R., W.K., C.H., C.M., S.R. and J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. The preparation of this paper was supported by the enable Cluster and is catalogued by the enable Steering Committee as enable 057 (http://enable-cluster.de). This work was funded by a grant of the German Ministry for Education and Research (BMBF) FK 01EA1807E. The present study was partly funded by the Federal Food Safety and Veterinary Office; project number 5.17.02ERN; project title "What does the Swiss population eat? Characterization of food consumption, dietary patterns and lifestyle in the Swiss language regions".

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

#### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

*Article*


**4. Experiments**

Experiments were made using classification and regression models. Moreover, user-independent and personal models were compared. The accuracies presented in this section are balanced accuracies, which is calculated by separately calculating accuracy for both classes and the reported balances accuracy is mean from these. Balanced accuracy was selected as the performance metric as it is not as vulnerable to unbalanced data as accuracy.

#### *4.1. User-Independent Stress Detection*

To compare regression and classification models, binary Random Forest classifier was trained and results of it were compared to Bagged Tree based ensemble regression model. Both were trained using leave-one-subject-out -method, meaning that in turn one person's data were used for testing and others data for training. For classification model, target values were transformed as 1 and 0, so that data from baseline are labelled as 0 and data from driving as 1. However, regression model was trained using continuous target values. Moreover, as the outputs of the regression model are continuous values, they were transformed as binary by finding an optimal threshold for each person to divide outputs as stress and non-stress that maximizes the accuracy. This was done by analyzing the obtained continuous prediction values study subject-wise, and such stress level value was searched which divides the prediction values as stressed and non-stressed so that the obtained balanced accuracy rate is as high high as possible. This of course over-estimates the performance of regression model as the threshold is calculated based on personal data but still it shows the potential of regression model based stress detection.

The results of user-independent recognition rates using regression and classification algorithms and different sensor combinations are presented in Table 2. Using leave-one-subject-out cross-validation method, a own recognition model was trained for each study subject and the performance of the model was calculated using balanced accuracy, sensitivity and specificity. The values presented in Table 2 shows the mean balanced accuracies, sensitivities and specificities calculated over all nine study subjects, and standard deviation between the study subjects is presented in parenthesis.

**Table 2.** Average recognition results accuracies, sensitivities and specificities (standard deviation in parentheses) using regression and classification model and sensor combinations.

Interestingly, according to Table 2 in both cases (regression and classification), the best recognition rates are obtained using only accelerometer features. This is not in line with previous stress detection studies, where it has been noted that features extracted biosignals are more useful for detecting stress than accelerometer features (see, for instance [19]). However, the reason for this is that, in this case, accelerometer-based models do not recognize stress at all; instead, they recognize the mode of transportation as baseline signal and stress signal are performed under different activity (sitting in non-moving car vs. driving a car). In fact, it is shown that these two activities can be recognized based on accelerometer data [20]. Therefore, as the aim of the study is to recognize stress, only models based on biosignals (=EDA, BVP, and ST) are worth studying.

Table 2 shows the potential of regression models. It outperforms classification algorithm with each sensor combination, and the highest recognition rates can be obtained using BVP and ST features. With this setting, the average balanced accuracy was 74.1% with classification model and 82.3% using regression model. In addition, sensitivity and specificity values are the highest using this combination. Confusion matrix for regression model for regression model using these features is shown in Table 3, where it can be seen that both classes are detected with reasonable accuracy. Moreover, according to Table 4 where user-independent accuracies are studied subject-wise using BVP and ST features, it can be noted that regression model performs better than classification model with all nine study subjects. Partly, the great performance of regression model is due to optimizing the threshold to classify outputs as stressed/non-stressed based on personal data but this is not the only reason for good performance of regression model. Another reason for it is that regression models gets more information as an input than classification model. In fact, classification models gets binary class labels as input while regression model's inputs are continuous targets. This results show that stress detection benefits from continuous targets and is as a nature a regression problem instead of classification problem. This was expected, as the level of the stress of a person can be high or low or anything between these.

**Table 3.** Confusion matrix, when user-independent regression model based on BVP and ST features is used for stress detection.

**Table 4.** Subject-wise balanced accuracy using user-independent model and a combination of BVP and ST features.

While regression models outperform classification models in stress detection, the real benefit of regression models is that, unlike classification, they can be used to estimate the level of stress. Figure 1 shows how well regression models estimate the level of the stress compared to user reported stress level values. The figure shows that in most cases the regression model has approximately managed to predict the level of the stress. However, this approximation is not very accurate, and it is inconsistent. This suggests that the the trained regression models is very sensitive to small changes in feature values, and due to this, the value of predicted stress level can rapidly change. Moreover, the approximation does not work at all for some study subjects (see study subjects MT, Figure 1d and SJ, Figure 1i). In the case of MT, the model has not managed to recognize the non-stressed stage, and in the case of SJ, the recognition of stressed stage has caused problems. These exceptions suggest that stress does not cause similar reactions to every person. Moreover, Figure 1 shows the personal threshold used to divide regression model outputs as stressed and non-stressed (black horizontal line). In most cases, the value of this threshold is between 0.3 and 0.5. However, also in this case MT and SJ are exceptions. In the case of MT, the value of threshold is close to 1, and in the case of SJ, the value is close to 0. This underlines how special these the cases are compared to other seven study subjects.

Table 5 shows root mean square error (RMSE) of predicted and user reported values for each study subject. As the stress level of the baseline was not measured during the data gathering, it was assumed that during the baseline measurement the level of the stress was constant and zero. This of course is just a best guess, and therefore, cannot be considered as fact. In fact, also the predictions shown in Figure 1 suggests that the level of the stress is not constant during the baseline and neither it is zero. Due to this, RMSE values of Table 5 are calculated from two parts of the signal: the second column shows the RMSE value from the whole signal and the third column shows the RMSE calculated only from driving part of the signal as only it contained target values defined by the driver. For instance, in the case of subject MT, there is a big difference between these two. Moreover, the R-Squared value was calculated from each subject, and they are shown in Table 5. These also underline the fact that prediction does not work for each subject. In fact, R-Squared value is zero for three study subjects indicating that for these persons user-independent model is not able to predict any variation on response. In overall, the results of the Table 5 show the potential of predicting the level of the stress using regression models. However, it also shows that at this point of the research, it only works for some study subjects and the

prediction can only give a rough estimation of the stress value, and it needs to be further studied how the exactness of the prediction could be improved.

**Figure 1.** Predicted stress level (blue line) vs. subject estimated continuous target value for stress level (orange line). Personal threshold used to divide outputs as stressed and non-stressed is shown using black horizontal line.

**Table 5.** Subject-wise RMSE and R-Squared using user-independent model and a combination of BVP and ST features.

The combination of BVP and ST features produced on average the highest recognition rates using user-independent regression model. However, this combination was not the best for each subject. Table 6 shows subject-wisely which sensor combination produces the best result. It can be seen that for most subjects, BVP + ST produces the best results but there is also exceptions. For instance for subject EK, a different selection of sensors improves the recognition rate over 20%-units (59.4 vs. 83.2, respectively). However, it has even bigger effect on standard deviation which is much smaller when features are selected subject-wise (17.0 vs. 11.7, respectively). Therefore, possibility to select features

subject-wise, and this way personalize recognition model, could potentially have a significant positive effect to the recognition rates.

**Table 6.** Best subject-wise recognition rates obtained using user-independent regression model, and which sensor combination was used to obtain this result.

#### *4.2. Personal Stress Detection*

The dataset contains more that one data gathering session from three study subjects (three sessions from subject NM (named NM1, NM2 and NM3), two from RY (named RY1 and RY2) and GM (named GM1 and GM2). Therefore, these three study subjects can be used to experiment if the stress recognition models trained using personal data are more accurate than user-independent models, as it is suggested in some of the papers. The data gathering protocol was the same in each of these session, and on each session, the study subjects drove the same route. However, what makes each session unique is that traffic was different in each case as the study subjects drove on public roads.

User-dependent recognition rates for NM, RY and GM are shown in Table 7 using different sensor combinations, and classification and regression models. In each case, models are trained with data from one session and tested with data from another session from the same user. NM1, RY1 and GM1 were used for testing, as these were also used in previous section to train and test user-independent models. According to Table 4 using user-independent model and a combination of BVP and ST features, which based on the Table 2 provide the highest average accuracy, Subject NM's stress can be detected with balanced accuracy of 64.3%/84.6% (classification vs. regression), RY's 90.8%/93.7%, and GM's 69.9%/95.0%. When these are compared to personal recognition rates based on the same features, it can be noted that personal training data do not have a big effect on the recognition rates. In fact, in the case of RY, the results based on personal model are much worse than the results based on user-independent model. Moreover, according to Table 7, the results using personal models are surprisingly bad no matter which sensor combination is used. There are some exceptions, though. RY's results are really good when using BVP features, and GM's stress can be recognized with high accuracy using a combination of BVP and ST features. However, the results from these users were already really good using user-independent model and usage of personal data do not have a big effect compared to those.

There can be several reasons for low personal recognition rates. For instance, in the case of data from subject GM, there seems to be problems with EDA signal, and thus, prediction using it always leads to really bad results. Another reason for low personal recognition rates can be that the size of the training data was not big enough, as in the case of personal model training data consist of measurement from one session and in user-independent case it consisted of measurements from eight sessions. In fact, when NM1 was trained using data from two personal data gathering sessions (NM2 and NM3), the results were much better than the one's reported in Table 7, and over 98% balanced accuracy was obtained using BVP and ST features, see Table 8. However, also in this case it can be noted that the recognition rate is highly dependent on which data are used for training and which for validation. It can be noted that when NM2 and NM3 are used for training and NM3 for testing, the recognition rates are really bad no matter which sensor combination is used. This shows that there are a lot of variation between data gathering sessions, even if the the data are collected from the same person.

**Table 7.** Recognition rates of users NM, RY, and GM using models trained using personal data.

**Table 8.** Cross-validation of three datasets collected from study subject NM.

#### **5. Discussion and Conclusions**

In this article, regression and classification models were compared for stress detection. Both personal and user-independent models were experimented. The article was based on publicly open dataset [14], which unlike most of the other stress detection datasets, contained continuous target variables. The used classification model was Random Forest and the regression model was Bagged tree based ensemble.

The study shows that regression models are superior to classification models when it comes to user-independent stress detection, if continuous target values are available. The best results were obtained using a combination of BVP and ST features, and using these the average balanced accuracy was 74.1% with classification model and 82.3% using regression model. In fact, basically no matter which sensor combination was used, the results using regression models were better than the one's obtained using classification models. This is because during the training process, regression model gets more information than classification model: regression model gets continuous targets as input while classification models inputs are discrete. Moreover, the results show that stress is not binary problem, a person can be highly stress, not stressed at all or anything between these two. Therefore, the results of this article show that prediction model should be trained so that all the available can be given as an input to it.

The main advantage of using regression models is that they can not only be used to recognize whether the person is stressed or not, they can also be used to predict the level of the stress. However, based on our experiments this prediction is not very accurate, and for some study subjects it does not work at all. In fact, the main goal of the future work is to study how the quality of this prediction could be improved. Due to this, different regression models needs to be compared to find

out which is the most capable to predict the level of the stress. This also includes comparison of different quantitative indicators to measures the goodness of the prediction. Unfortunately, continuous target variables are rarely available for the datasets which is the reason why regression models are not often used for stress detection, and why stress detection based on classification models is an important topic to study also in the future.

Stress detection based on personal training data was also studied using data from three persons. The results obtained using personal models were surprisingly bad. The situation was better when more personal data were used for training but still there was a lot of variation in the recognition rates depending on how data gathering sessions were divided for training and testing. This shows that biosignals have a lot of variation not only between the study subjects but also between the session gathered from the same person. Therefore, personal recognition models should be able to constantly adapt to changes in human body. Due to this, for instance incremental learning-based method should be experimented [21]. On the other hand, it was shown that subject-wise feature selection for user-independent model can be a better approach than usage of personal training data to personalize and improve recognition models. It was noted that with subject-wise feature selection, the average detection rate improved 4%-units, and it was especially useful to reduce the variance in the recognition rates between the study subjects.

Moreover, it can be noted that the recognition rates obtained in this article are not as high as the one's obtained in several other stress detection studies where the same features are used ([7,19]). The reason for this can be used data gathering protocol. In many other studies, the stress data are collected from contexts (giving public speech, performing difficult arithmetic tasks, etc.) that most likely are more stressful than driving a car. Therefore, the dataset used in this study does as big difference between relaxed and stressed state as other datasets making it more difficult to analyze. It is also possible that the type of the stress is different in these contexts. In fact, part of the future work is to make experiments using different datasets. In fact, to extend this study, these datasets should include data from new types of sensors (for instance electroencephalography (EEG)), to show that the proposed method is not dependent on the used sensor. The expected result is that the proposed method works similar to this study if appropriate features are extracted from the user sensor. In fact, extracted features needs to be selected sensor-wise, and though, the same features should not be extracted from each sensor. In addition, the experimented datasets should contain data not only from stress, but also from other affect states to show that the proposed method is not dependent on the studied affect state. The final aim is to detect multiple affect states from at same time, for example a person can be stressed and angry. This could be done for instance by training an own regression model for each affect state.

Lastly, it should be noted that when dealing data from stress and affect states which is labeled by the study subjects themselves, it should be noted that study subjects do not necessarily know what they feel [22]. Therefore, the target variables obtained from the study subjects can be unreliable. Due to this, while the preliminary results presented in this article are encouraging, more experiments with more datasets should be carried out to get a better understanding of how accurate the presented method actually is.

**Author Contributions:** Conceptualization, P.S.; Formal analysis, P.S.; Supervision, J.R.; Validation, P.S.; Writing–original draft, P.S.; Writing–review & editing, J.R. Both authors have read and agreed to the published version of the manuscript.

**Funding:** This research is supported by the Business Finland funding for Reboot IoT Factory-project (www. rebootiotfactory.fi).

**Acknowledgments:** Authors are thankful for Infotech Oulu. Authors would also like to thank Neska El Haouij for collecting a great dataset and helping us to connect the right observations for the right target values.

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