\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={Cardiometabolic Biological Risk Factors, Unhealthy Lifestyle, and Antipsychotic Drug use among People with Psychotic Disorders in Cameroon},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2021 2021-01-15 (revised: 9 Year 2021 15 January 2021)}
\def\TheID{\makeatother }
\def\TheDate{2021 2021-01-15}
\title{Cardiometabolic Biological Risk Factors, Unhealthy Lifestyle, and Antipsychotic Drug use among People with Psychotic Disorders in Cameroon}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{Hermine Raissa Hell, Maxwell Nguedjo Wandji, Boris Ronald Tchu ente Tonou, Celine Sylvie Mimboe Bilongo, Hippolyte Tene Mouafo, Abomo Anne-Christine Ndzana, Jean Pierre Olen Kamga, Olga Yvonne Mank ollo  Bassong}

\renewcommand\Authands{ and }

\date{\small \em Received: 11 December 2020 Accepted: 5 January 2021 Published: 15 January 2021}

\maketitle


\begin{abstract}
        


Background: The aim of this study was to assess the prevalence of cardiometabolic risk factors and examine whether the presence of metabolic abnormalities is associated with antipsychotic drug use andthe lifestyle of patients with psychotic disorders.Methods: This study was cross-sectional and was carried out at the Jamot Hospital in Yaounde (Cameroon), on patients with psychotic disorders and under treatment. Parameters performed on participants were physical examination and fasting glucose.

\end{abstract}


\keywords{cardiometabolic biological risk factors, unhealthy lifestyle, antipsychotic drugs, psychotic disorders.}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\let\tabcellsep& 	 	 		 
\section[{Introduction 1}]{Introduction 1}\par
Year 2021 all classes of the population. However, recent studies show that they are higher in patients suffering from psychotic disorders than in the general population \hyperref[b1]{[2]}. This high CVD rate contributes significantly to impair health status and reduces life expectancy, resulting in early death in people with psychosis \hyperref[b2]{[3]}. From an epidemiological perspective, the increased prevalence of CVD might associated with cardiometabolic biological risk factors such as hypertension, hyperglycemia, dyslipidemia, and obesity \hyperref[b3]{[4]}. These metabolic abnormalities might also be influenced by psychiatric conditions and an unhealthy lifestyle characterized by a high-calorie diet high in saturated fat, tobacco and alcohol consumption, and lack of physical exercise \hyperref[b5]{[5,}\hyperref[b6]{6]}. However, other factors such as antipsychotic medications may also exacerbate the onset of metabolic disorders, with significant weight gain, lipid disorders, and alterations in blood glucose levels \hyperref[b7]{[7]}. Indeed, numerous studies have shown that people with psychotic disorders and under treatment have a substantial risk of death due to CVD \hyperref[b8]{[8]} and an increased risk of developing diabetes, hypertension, and hyperlipidemia \hyperref[b9]{[9]}. A meta-analysis revealed a prevalence of 44\% abdominal obesity, 19.5\% hyperglycemia, and 39\% hypertension among people with psychotic disorders worldwide \hyperref[b11]{[10]}.\par
In sub-Saharan Africa, the prevalence of cognitive impairment in the population was between 6.3\% and 25\% \hyperref[b33]{[11]}, and of dementia between 2.29\% and 21.60\%  {\ref [12]}. It suggests that the population of African countries and particularly those from Cameroon are not free from this scourge \hyperref[b12]{[13]}. However, developing an effective control strategy requires scientific data on the cardiometabolic of cardio metabolic risk factors in patients with psychotic disorders and the determinants of their progression specific to the socio-cultural context. Thus, it is of concern that cardiovascular disease is not well described in this population and the low rate of metabolic screening. Therefore, to fill these gaps, our study aims to determine the prevalence of cardio metabolic risk factors and examine whether or not the II. 
\section[{Materials and Methods}]{Materials and Methods} 
\section[{III.}]{III.} 
\section[{Data Collection}]{Data Collection}\par
IV. 
\section[{Measurements a) Anthropometric measurements b) Arterial blood pressure measurements}]{Measurements a) Anthropometric measurements b) Arterial blood pressure measurements}\par
Blood pressure was measured using an OMRON electronic radial sphygmomanometer at rest. The participant was seated in a chair with the left arm parallel to the heart. The measurement was taken at the beginning of the interview after a 10-minute rest. The values obtained on the dial of the device were used to assess the presence or absence of hypertension. 
\section[{c) Biochemical Analysis}]{c) Biochemical Analysis}\par
Fasting blood glucose was measured by the glucose oxidase method of Trinder \hyperref[b13]{[14]} using a glucose meter and test strips (Gluco Plus®) directly on the participant's fingertip. 
\section[{e) Statistical analysis}]{e) Statistical analysis}\par
V. 
\section[{Results}]{Results}\par
The study population included 82 patients with psychotic disorders of which 41 were women and 41 presence of metabolic abnormalities is associated with antipsychotic drug use and lifestyle in this vulnerable population. 
\section[{a) Study design and Recruitment}]{a) Study design and Recruitment}\par
A cross-sectional and descriptive study carried on throughout January 2018 in the psychiatric ward of the Jamot Hospital in Yaoundé, (Central Region of Cameroon), which is a reference hospital for psychiatry in Cameroon. Patient recruitment was based on antipsychotic drugs, the absence of mental instability, and the absence of a diabetogenic diagnosis before the start of treatment. A total of 82 patients of both sexes, aged at least 18 years and suffering from psychotic disorders, were selected. 
\section[{b) Ethical Consideration}]{b) Ethical Consideration}\par
The study was approved by the National Ethics Committee for Research in Human Health of Cameroon under N°2017/0588/CEIRSH/ESS/MSP and it was authorized by the Director of Jamot Hospital, Yaoundé under the reference n°00001731/MINSANTE/SG/DHJY. Free and informed consent was obtained from each patient, or their parent or legal guardian to participate in the study. The study was carried on in strict compliance with medical ethics following the Declaration of Helsinki. 
\section[{a) Questionnaire}]{a) Questionnaire}\par
After obtaining free and voluntary consent from the participants involved in this study, well-structured questionnaires were administered by well-trained health staff. The questionnaire was adapted from the WHO Stepwise approach for surveillance of risk factors for nutrition-related chronic disease-Instrument V2.1. Participants' information on age, sex, consumption of tobacco, alcohol, fruits, and vegetables, and the antipsychotic drugs administered were collected. Antipsychotic drugs were grouped into two types: typical antipsychotics and atypical antipsychotics. Alcohol and tobacco consumption was classified into two categories: non-drinkers and drinkers and nonsmokers and smokers, respectively. The participant's consumption of carried on and vegetables was grouped based on weekly intake. From 0 to 1 time per week, was classified as irregular; ? 2 times/week , it was classified as regular. Based on the WHO Global Physical Activity Questionnaire (GPAQ) Analysis Guide, participants were classified as having low and high levels of physical activity by using the information related to their principal activity at work, transportation, leisure activities, and sitting.\par
Participants Weight was measured to the nearest 0.1 kg using a TANITA TM personal scale. Their Height was recorded using a SECA vertical scale graduated to the nearest centimeter. Body mass index (BMI) was calculated as weight using the formula: BMI = Weight (kg)/Height 2 (m) and expressed in kg/m 2 . The waist circumference was measured using a tape on the midpoint between the lower rib margin and the iliac crest in a perpendicular plane to the long axis of the body without restrictive garments. 
\section[{d) Diagnosis of cardiometabolic risk factors}]{d) Diagnosis of cardiometabolic risk factors}\par
Nutritional status has been defined according to WHO criteria as follows: overweight has been defined as a BMI between 25 and 29.9 kg/m 2 and has as a BMI ? of 30 kg/m 2 \hyperref[b14]{[15]}. Other metabolic abnormalities were diagnosed according to the criteria of the National Cholesterol Education Program Adult Treatment Panel III as follows: Abdominal obesity, determined by high waist circumference (>102 cm for men and>88 cm for women); Hyperglycemia, (fasting blood glucose?110 mg/dL) and Hypertension, (high blood pressure: Systolic Blood Pressure (SBP)?130 mmHg and Diastolic Blood Pressure (DBP) ? 85 mmHg) \hyperref[b15]{[16]}.\par
The data was analyzed using SPSS version 25.0 for Windows. The results were showed as a mean ± standard deviation (SD) for continuous variables and as a frequency (\%) for categorical variables. The Pearson chi-square test was used to compare proportions between categorical variables. The student's t-test was used to detect differences in between two groups of a continuous dependent variable. Bivariate logistic regressions were used to assess the risk of developing metabolic abnormalities. The significance level used was p<0.05.   As shown in Table \hyperref[tab_1]{2}, the prevalence of overweight and obesity was significantly higher in women (48.8\% and 29.3\%) than in men (22\% and 7.3\%, respectively)\par
Furthermore, the prevalence of hyperglycemia was significantly higher in men (53.7\%) then in women (31.7\%). In the general population, 35.4\% (n=29) of participants were overweigh (25? BMI?29kg/m 2 ), \hyperref[b17]{18}.3\% (n=15) were obese (BMI ? 30kg/m 2 ) and 42.7\% (n=35) had hyperglycemia. However, high blood pressure was more prevalent in the general population 54.9\% (n=45), with systolic hypertension predominating 48.8\% (n=40). 
\section[{Biological factors}]{Biological factors}\par
The majority of the study population was sedentary (63.4\%) and women were less active than men (82.9\% vs. 43.9\%) (Table \hyperref[tab_2]{3}). The prevalence of Tobacco and alcohol consumption was 39\% and 58.5\%, respectively in the general population and the highest antipsychotic medication were more significantly (p<0.05) affected by systolic hypertension (61.1\%), diastolic hypertension (59.3\%), and high blood pressure 27.3(3) 0.657 0.003* 
\section[{P-v alue t*a: p-value observed for comparison between patients on typical and atypical antipsychotics}]{P-v alue t*a: p-value observed for comparison between patients on typical and atypical antipsychotics}\par
Table \hyperref[tab_5]{5} shows that participants on typical antipsychotics consumed significantly more alcohol (68.5\%) than those on atypical antipsychotics (39.3\%).\par
Moreover, regardless of antipsychotic treatment, women were significantly less active than men (p < 0.05).   
\section[{Treatment with antypsychotics Biological risk factors}]{Treatment with antypsychotics Biological risk factors}\par
The influence of an unhealthy lifestyle on the risk of developing metabolic pathologies is illustrated in Table \hyperref[tab_7]{7}. Globally, tobacco consumption increased the risk of developing hyperglycemia (OR=2.01; 95\% CI: 0.81-4.97); diastolic hypertension (OR=1.92; 95\% CI: 0.78-4.73) and hypertension (OR=1.34; 95\% CI: 0.55-3.31). A sedentary lifestyle increased the risk of being overweight (OR=5.92; 95\% CI: 1.86-18.84) and obese (OR=3.39; 95\% CI: 0.91-12.60). However, the consumption of alcohol and the irregular consumption of fruit/vegetable does not influence the occurrence of cardio metabolic biological risk factors in the study population.  
\section[{Global Journal of}]{Global Journal of}\par
Cardiovascular diseases are known as one of the most common causes of premature death and great health concerns \hyperref[b0]{[1]}. These cardiovascular diseases are due to a propensity for cardiometabolic risk factors. These can be exacerbated by a state of psychosis and a poor lifestyle \hyperref[b5]{[5,}\hyperref[b6]{6]}. This study aimed to determine the prevalence of cardiometabolic risk factors and examine whether the presence of metabolic abnormalities is associated with antipsychotic drug use or lifestyle in this vulnerable population.\par
The state of cardiometabolic risk factors in Cameroonian patients with psychotic disorders revealed that low physical activity was predominant in this population (63.4\%). It could be due to the side effect of antipsychotic drugs, which included fatigue. Psychiatric symptoms and the severity of mental illness could also explain the participants' inactivity \hyperref[b16]{[17]}. High levels of physical inactivity in mentally ill patients and under treatment were also observed by Nyboe and Lund \hyperref[b17]{[18]}. Independently of the sensitization campaign on the side effects of tobacco and alcohol, it was found that 58.5 \% of the subjects consumed alcohol and 39 \% were smokers. The reason behind this could be the westernization of lifestyle habits and especially the popularisation of these products. However, the beneficial effect of the nicotine contained in tobacco on cognition and mood could also explain the fact that these persons with psychotic disorders often smoked tobacco \hyperref[b18]{[19]}. An increase in tobacco and alcohol consumption among patients suffering from psychotic disorders than the general population was highlighted by Hartz et al. \hyperref[b19]{[20]}. Half of the participants in this study had a low intake of fruits and vegetables. Indeed, low fruit and vegetable consumption associated with tobacco and alcohol consumption have been reported in people with psychosis \hyperref[b20]{[21]}. Likewise, a low-fiber diet was found in these patients \hyperref[b21]{[22]}. In general, low fruit and vegetable consumption accompanied by unhealthy behaviors have been observed in patients undergoing treatment with typical and atypical antipsychotic drugs. It would be attributed much more to the mental illness itself than to the use of antipsychotic medication.\par
Regarding cardiometabolic biological risk factors, 35.4\% of patients were overweight, and 18.3\% were obese. Overall, among the overweight or obese participants, the majority were women. An observation of the physical activity data also showed that women were more inactive. Therefore, women's physical inactivity may account for their high prevalence of obesity. Sultani et al. \hyperref[b22]{[23]} also observed a significant association between low physical activity and obesity in people with psychotic illnesses. In this study, the irregular consumption of fruits and vegetables (less than once a week) by 50\% of the participants could also justify the high prevalence of obesity. Indeed, high consumption of fruit will provide the body with dietary fiber which will increase satiety leading to a reduction in total energy intake and prevents weight gain \hyperref[b23]{[24]}. On the other hand, the overweight observed in patients under antipsychotic drugs is probably secondary to an increase in food consumption due to the stimulant effect of the drugs. The action of antipsychotic drugs is believed to involve a blockade of dopamine and monoamine receptors leading to a potential increase in appetite with weight gain as an inference \hyperref[b24]{[25]}. Sicras-Mainar et al. \hyperref[b25]{[26]} showed that body mass index was significantly elevated in patients on antipsychotic medication and that obesity was associated with antipsychotic medication use. However, our results revealed that overweight was more prevalent among participants on atypical antipsychotic medication. This might be explained by the concomitant blocking of dopamine and serotonin receptors by atypical antipsychotics, which contribute to an increased risk of overweight and obesity, as opposed to typical antipsychotics that are more specific to dopamine receptors \hyperref[b26]{[27]}.\par
The occurrence of overweight due to the combined effect of low physical activity, antipsychotic medication, and a diet low in fruits and vegetables could also explain the presence of abdominal obesity in the participants via adipose tissue dysfunction leading to fat redistribution preferentially in the abdomen [28]. However, abdominal obesity was more prevalent in men (46.3\%) than to women (36.6\%) regardless of the type of antipsychotic drug administered. This could be explained by the heavy consumption of tobacco (48.8\%) and alcohol (63.4\%) in men. Nicotine is thought to affect fat distribution through its anti-estrogenic effect, which favors android-type fat distribution, and through the increase in stress hormones such as cortisol \hyperref[b29]{[29]}. Also, alcohol consumption inhibits fat oxidation, thus promoting the accumulation and retention of lipids in visceral adipose tissue \hyperref[b30]{[30]}.\par
Participants were also affected by hyperglycemia (42.7\%). Smoking in study patients could explain this abnormality in carbohydrate metabolism by inducing oxidative stress due to an increase in reactive oxygen species, be the cause of insulin resistance leading to the deregulation of blood glucose homeostasis \hyperref[b31]{[31]}. It supports our results, which showed that tobacco consumption increased the risk of developing hyperglycemia by 2.01 times. However, men were more exposed to hyperglycemia (53.7\%) than to women (31.7\%). Smoking associated with the presence of more abdominal fat accumulation prevalent in men would be the cause. Indeed, it has been shown that abdominal obesity induces an excess of circulating free fatty acid, which is directly deleterious to insulin signaling, thus inhibiting GLUT4 translocation, resulting in the development of hyperglycemia \hyperref[b32]{[32]}   
\section[{Conclusion}]{Conclusion}\par
This study revealed that Cameroonians with psychotic disorders are prone to cardiometabolic biological risk factors, including overweight, obesity, abdominal obesity, hypertension, and hyperglycemia. However, an unhealthy lifestyle characterized by low levels of physical activity and tobacco use, combined with antipsychotic medication, particularly atypical antipsychotics, contribute significantly to the occurrence of cardiometabolic biological risk factors. In light of these observations, it is important for healthcare providers to be aware of the potential cardiovascular adverse effects of antipsychotics and bad lifestyle behaviors, and to monitor and address Cardiometabolic biological risk factors in patients with psychosis. This study suggests that important steps should be taken by the government to reduce the morbidity and mortality of patients suffering from psychosis, and therefore consider the findings of this study to better improve the care and management of people suffering from psychosis in Cameroon. These measures should take into consideration the specific needs according to the socio-cultural context contribute to hyperglycemia through weight gain since excess body fat is known to be a risk factor for many metabolic disturbances such as hyperglycemia \hyperref[b37]{[33]}. Indeed, Lorraine et al. \hyperref[b34]{[34]} showed that patients under antipsychotic treatment were associated with a high risk of hyperglycemia.\par
Hypertension was the most prevalent cardiometabolic biological risk factor in this population (54.9\%). This result could be explained by tobacco consumption, which exerts a persistent effect of pressure and tachycardia, by a mechanism that involves stimulation of the sympathetic nervous system with a consequent increase in blood pressure \hyperref[b35]{[35]}. The high prevalence of hypertension could also be explained by the use of antipsychotic medication. Although the mechanisms by which antipsychotic drugs cause hypertension is not yet well understood, several studies have shown that patients on antipsychotic drugs (aripiprazole) have developed hypertension  {\ref [36]}.\begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.20219399538106236\textwidth}P{0.19041570438799077\textwidth}P{0.17863741339491918\textwidth}P{0.1766743648960739\textwidth}P{0.10207852193995381\textwidth}}
Parameters\tabcellsep Population\tabcellsep Women\tabcellsep Men\tabcellsep P-value\\
Age (years)\tabcellsep 32.70±10.73\tabcellsep 33.07±9.67\tabcellsep 32.32±11.81\tabcellsep 0.752\\
Weight (kg)\tabcellsep 75.82±14.38\tabcellsep 78.83±15.88\tabcellsep 72.80±12.16\tabcellsep 0.057\\
Height (m)\tabcellsep 1.72±0.11\tabcellsep 1.67±0.10\tabcellsep 1.72±0.09\tabcellsep 0.0001*\\
BMI (kg/m 2 )\tabcellsep 26.30±5.20\tabcellsep 28.72±5.56\tabcellsep 23.89±3.45\tabcellsep 0.0001*\\
WC (cm)\tabcellsep 92.73±14.43\tabcellsep 96.93±14.74\tabcellsep 88.54±12.97\tabcellsep 0.008*\\
Blood glucose (mg/dL)\tabcellsep 108.26±23.93\tabcellsep 104.83±22.84\tabcellsep 111.68±24.78\tabcellsep 0.197\\
SBP (mmHg)\tabcellsep 132.51±25.19\tabcellsep 131.71±20.84\tabcellsep 133.32±29.13\tabcellsep 0.774\\
DBP (mmHg)\tabcellsep 80.41±14.66\tabcellsep 79.66±16.09\tabcellsep 81.17±13.22\tabcellsep 0.643\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.18858520900321543\textwidth}P{0.11479099678456592\textwidth}P{0.39766881028938905\textwidth}P{0.0847266881028939\textwidth}P{0.06422829581993568\textwidth}}
\tabcellsep \multicolumn{2}{l}{Population \%(n) Women \%(n)}\tabcellsep Men \%(n)\tabcellsep P-value\\
Overweight(25? BMI?29kg/m 2 )\tabcellsep 35.4(29)\tabcellsep 48.8(20)\tabcellsep 22.0(9)\tabcellsep 0.0001*\\
Obesity(BMI ? 30kg/m 2 )\tabcellsep 18.3(15)\tabcellsep 29.3(12)\tabcellsep 7.3(3)\tabcellsep 0.0001*\\
Abdominal obesity\tabcellsep 41.5(34)\tabcellsep 36.6(15)\tabcellsep 46.3(19)\tabcellsep 0.370\\
Hyperglycemia\tabcellsep 42.7(35)\tabcellsep 31.7(13)\tabcellsep 53.7(22)\tabcellsep 0.044*\\
Systolic hypertension\tabcellsep 48.8(40)\tabcellsep 51.2(21)\tabcellsep 46.3(19)\tabcellsep 0.659\\
Diastolic hypertension\tabcellsep 46.3(38)\tabcellsep 41.5(17)\tabcellsep 51.2(21)\tabcellsep 0.376\\
Hypertension\tabcellsep 54.9(45)\tabcellsep 53.7(22)\tabcellsep 56.1(23)\tabcellsep 0.824\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{consumption scores were recorded with men 48.8\% and}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{63.4\% for tobacco and alcohol, respectively. 50\% of the}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{participants had an irregular consumption of}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{fruit/legumes. This irregular consumption was more}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{pronounced among women (51.2\%).}\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.4013114754098361\textwidth}P{0.13377049180327868\textwidth}P{0.11983606557377048\textwidth}P{0.11426229508196721\textwidth}P{0.08081967213114753\textwidth}}
Factors of unhealthy lifestyle\tabcellsep Population \%(n)\tabcellsep Women \%(n)\tabcellsep Men \%(n)\tabcellsep P-value\\
Consumption of tobacco\tabcellsep 39.0(32)\tabcellsep 29.3(12)\tabcellsep 48.8(20)\tabcellsep 0.070\\
Consumption of alcohol\tabcellsep 58.5(48)\tabcellsep 53.7(22)\tabcellsep 63.4(26)\tabcellsep 0.370\\
Irregular consumption of fruit/vegetable\tabcellsep 50.0(41)\tabcellsep 51.2(21)\tabcellsep 48.8(20)\tabcellsep 0.825\\
Low level of physical activity\tabcellsep 63.4(52)\tabcellsep 82.9(34)\tabcellsep 43.9(18)\tabcellsep 0.0001*\end{longtable} \par
  {\small\itshape [Note: L© 2021 Global Journals]} 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.36994818652849737\textwidth}P{0.4800518134715026\textwidth}}
shows that participants under atypical\tabcellsep overweight (46.4\%), obesity (28.6\%), and hyperglycemia\\
antipsychotic medication were more affected by\tabcellsep (57.1\%) regardless of gender. Patients under typical\end{longtable} \par
 
\caption{\label{tab_3}Table 4}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
P-value\\
t*a\end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.85\textwidth}}
P-\\
value\\
t*a\end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.85\textwidth}}
presents the occurrence risk of cardio\\
metabolic biological risk factors associated with using a\\
type of antipsychotic drug. The use of the atypical\\
antipsychotic drug increased the risk of overweight\\
(OR=3.59; 95\% CI: 1.19-10.80), obesity (OR=5.06; 95\%\\
CI: 1.37-18.65), and hyperglycemia (OR=2.45; 95\% CI:\\
0.96-6.24).\end{longtable} \par
 
\caption{\label{tab_6}Table 6}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.0292806484295846\textwidth}P{0.06458966565349544\textwidth}P{0.04047619047619047\textwidth}P{0.06286727456940223\textwidth}P{0.022391084093211753\textwidth}P{0.023252279635258357\textwidth}P{0.055116514690982776\textwidth}P{0.013779128672745694\textwidth}P{0.0439209726443769\textwidth}P{0.03875379939209726\textwidth}P{0.024113475177304965\textwidth}P{0.027558257345491388\textwidth}P{0.03789260385005065\textwidth}P{0.013779128672745694\textwidth}P{0.033586626139817624\textwidth}P{0.03789260385005065\textwidth}P{0.013779128672745694\textwidth}P{0.04047619047619047\textwidth}P{0.03789260385005065\textwidth}P{0.013779128672745694\textwidth}P{0.04133738601823708\textwidth}P{0.03789260385005065\textwidth}P{0.013779128672745694\textwidth}P{0.03272543059777103\textwidth}P{0.04908814589665653\textwidth}}
\tabcellsep \tabcellsep P-\tabcellsep value\tabcellsep \tabcellsep 0.003*\tabcellsep \tabcellsep \tabcellsep 0.067\tabcellsep \tabcellsep \tabcellsep 0.838\tabcellsep \tabcellsep \tabcellsep 0.580\tabcellsep \tabcellsep \tabcellsep 0.279\tabcellsep \tabcellsep \tabcellsep 0.614\tabcellsep \tabcellsep \tabcellsep 0.480\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep OR (95\%)\tabcellsep 5.92\tabcellsep \tabcellsep (1.86-18.84)\tabcellsep 3.39\tabcellsep \tabcellsep (0.91-12.60)\tabcellsep 1.10\tabcellsep \tabcellsep (0.44-2.74)\tabcellsep 0.77\tabcellsep \tabcellsep (0.31-1.91)\tabcellsep 0.60\tabcellsep \tabcellsep (0.24-1.50)\tabcellsep 0.79\tabcellsep \tabcellsep (0.32-1.95)\tabcellsep 0.72\tabcellsep \tabcellsep (0.29-1.79)\\
\tabcellsep \tabcellsep \tabcellsep ref\tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \\
6 Year 2021\tabcellsep Consumption of fruit/vegetable\tabcellsep Regular Irregular P-\tabcellsep ref OR (95\%) value\tabcellsep 0.84\tabcellsep 1 0.724\tabcellsep (0.31-2.21)\tabcellsep 0.78\tabcellsep 1 0.696\tabcellsep (0.23-2.60)\tabcellsep 1.00 1.000\tabcellsep 1\tabcellsep (0.41-2.40)\tabcellsep 0.73\tabcellsep 1 0.503\tabcellsep (0.30-1.78)\tabcellsep 1.00\tabcellsep 1 1.000\tabcellsep (0.42-2.37)\tabcellsep 0.67\tabcellsep 1 0.376\tabcellsep (0.28-1.61)\tabcellsep 0.90\tabcellsep 1 0.824\tabcellsep (0.38-2.16)\\
the unhealthy lifestyle\tabcellsep \tabcellsep No P-\tabcellsep ref OR (95\%) value\tabcellsep 0.64\tabcellsep 1 0.378\tabcellsep (0.23-1.72)\tabcellsep 0.45\tabcellsep 1 0.204\tabcellsep (0.13-1.53)\tabcellsep 0.67\tabcellsep 1 0.388\tabcellsep (0.27-1.64)\tabcellsep 0.73\tabcellsep 1 0.501\tabcellsep (0.30-1.79)\tabcellsep 0.92\tabcellsep 1 0.853\tabcellsep (0.38-2.21)\tabcellsep 0.95\tabcellsep 1 0.913\tabcellsep (0.39-2.29)\tabcellsep 0.93\tabcellsep 1 0.878\tabcellsep (0.38-2.25)\\
\tabcellsep Tobacco consumption\tabcellsep Yes P-\tabcellsep OR (95\%) value\tabcellsep 0.61\tabcellsep 0.326\tabcellsep (0.22-1.63)\tabcellsep 0.15\tabcellsep 0.023*\tabcellsep (0.03-0.77)\tabcellsep 0.61 0.299\tabcellsep \tabcellsep (0.24-1.53)\tabcellsep 2.01\tabcellsep 0.128\tabcellsep (0.81-4.97)\tabcellsep 1.08\tabcellsep 0.860\tabcellsep (0.44-2.63)\tabcellsep 1.92\tabcellsep 0.152\tabcellsep (0.78-4.73)\tabcellsep 1.34\tabcellsep 0.513\tabcellsep (0.55-3.31)\\
\tabcellsep \tabcellsep No\tabcellsep ref\tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \\
\tabcellsep Cardiometabolic biological\tabcellsep risk factors\tabcellsep \tabcellsep Overweight\tabcellsep \tabcellsep (25? BMI?29kg/m 2 )\tabcellsep \tabcellsep ) Obesity(BMI ? 30kg/m 2\tabcellsep \tabcellsep \tabcellsep Abdominal obesity\tabcellsep \tabcellsep \tabcellsep Hyperglycemia\tabcellsep \tabcellsep \tabcellsep Systolic hypertension\tabcellsep \tabcellsep \tabcellsep Diastolic hypertension\tabcellsep \tabcellsep \tabcellsep Hypertension\tabcellsep ref=reference\end{longtable} \par
 
\caption{\label{tab_7}Table 7 :}\end{figure}
 		 		\backmatter  			 
\subsection[{Authors' Contributions}]{Authors' Contributions}\par
OYMB, KOJP, RHH designed and conducted the study; RHH collected the data; MWN, RBTT, and AACN conducted the statistical analysis of the data; MWN, RBTT, RHH, and CMSB wrote the manuscript. All authors read and approved the final manuscript. Funding This work has not been supported by a grant or funding. 
\subsection[{Conflict of interest}]{Conflict of interest}\par
The authors declare that there is no conflict of interest regarding the publication of this document.			 			  				\begin{bibitemlist}{1}
\bibitem[Liao et al. ()]{b9}\label{b9} 	 		\textit{},  		 			C.-H Liao 		,  		 			C.-S Chang 		,  		 			W.-C Wei 		,  		 			S.-N Chang 		,  		 			C.-C Liao 		,  		 			H.-Y Lane 		,  		 			F.-C Sung 		.  		2011.  	 
\bibitem[Mucheru et al. ()]{b3}\label{b3} 	 		\textit{},  		 			D Mucheru 		,  		 			M-C Hanlon 		,  		 			L E Campbell 		,  		 			M Mc Evoy 		,  		 			L Donald-Wicks 		.  		2018.  	 
\bibitem[Longo et al. ()]{b27}\label{b27} 	 		\textit{},  		 			M Longo 		,  		 			F Zatterale 		,  		 			J Naderi 		,  		 			L Parrillo 		,  		 			P Formisano 		,  		 			G A Raciti 		,  		 			C Miele 		.  		2019.  	 
\bibitem[Adipose Tissue Dysfunction as Determinant of Obesity-Associated Metabolic Complications International Journal of Molecular Sciences]{b28}\label{b28} 	 		‘Adipose Tissue Dysfunction as Determinant of Obesity-Associated Metabolic Complications’.  		 \xref{http://dx.doi.org/10.3390/ijms20092358}{10.3390/ijms20092358}.  	 	 		\textit{International Journal of Molecular Sciences}  		20  (9)  p. 2358.  	 
\bibitem[Han et al. ()]{b37}\label{b37} 	 		‘Analysis of obesity and hyperinsulinemia in the development of metabolic syndrome: San Antonio Heart Study’.  		 			T Han 		,  		 			K Williams 		,  		 			N Sattar 		,  		 			K Hunt 		,  		 			M Lean 		,  		 			R S Haffne 		.  	 	 		\textit{Obes. Res}  		2002. 10 p. .  	 
\bibitem[Lorraine et al. ()]{b34}\label{b34} 	 		‘Antipsychotic Drugs and Hyperglycemia in Older Patients with Diabetes’.  		 			L L Lorraine 		,  		 			L V Linda 		,  		 			G Andrea 		,  		 			D F Hadasv 		,  		 			N J David 		,  		 			S G Sudeep 		,  		 			H Nathan 		,  		 			E H Janet 		,  		 			M A Geoff 		,  		 			A R Paula 		.  	 	 		\textit{Arch Intern Med}  		2009. 2009. 169  (14)  p. .  	 
\bibitem[Liberty et al. ()]{b26}\label{b26} 	 		‘Atypical Antipsychotics and Diabetes Mellitus: An Association’.  		 			F I Liberty 		,  		 			D Todder 		,  		 			R Umansky 		,  		 			I Harman-Boehm 		.  	 	 		\textit{IMAJ}  		2004. 6 p. .  	 
\bibitem[Galletly et al. ()]{b2}\label{b2} 	 		‘Cardiometabolic risk factors in people with psychotic disorders: The second Australian national survey of psychosis’.  		 			C A Galletly 		,  		 			D L Foley 		,  		 			A Waterreus 		,  		 			G F Watts 		,  		 			D J Castle 		,  		 			J J Mc Grath 		,  		 			V A Morgan 		.  		 doi:10.117 7/0004867412453089.  	 	 		\textit{Australian \& New Zealand Journal of Psychiatry}  		2012. 46  (8)  p. .  	 
\bibitem[Sileshi ()]{b1}\label{b1} 	 		‘Cardiovascular disease among severe mental illness and psychiatric medication’.  		 			D Sileshi 		.  		 \xref{http://dx.doi.org/10.5334/gh.403}{10.5334/gh.403}.  	 	 		\textit{Global Journal of Medicine and Public Health}  		 \#-2277-9604.  		2017. 6  (6) .  	 
\bibitem[Cardiovascular disease lifestyle risk factors in people with psychosis: a cross-sectional study BMC Public Health]{b4}\label{b4} 	 		‘Cardiovascular disease lifestyle risk factors in people with psychosis: a cross-sectional study’.  		 \xref{http://dx.doi.org/10.1186/s12889-018-5649-5}{10.1186/s12889-018-5649-5}.  	 	 		\textit{BMC Public Health}  		18  (1) .  	 
\bibitem[Yuyun et al. ()]{b0}\label{b0} 	 		‘Cardiovascular Diseases in Sub-Saharan Africa Compared to High-Income Countries: An Epidemiological Perspective’.  		 			M F Yuyun 		,  		 			K Sliwa 		,  		 			A P Kengne 		,  		 			A O Mocumbi 		,  		 			G Bukhman 		.  		 \xref{http://dx.doi.org/10.5334/gh.403}{10.5334/gh.403}.  		 \url{https://doi.org/10.5334/gh.403}  	 	 		\textit{Global Heart}  		2020. 15  (1)  p. 15.  	 
\bibitem[Howell et al. ()]{b8}\label{b8} 	 		‘Cardiovascular effects of psychotic illnesses and antipsychotic therapy’.  		 			S Howell 		,  		 			E Yarovova 		,  		 			A Khwanda 		,  		 			S D Rosen 		.  		 \xref{http://dx.doi.org/10.1136/heartjnl-2017-312107}{10.1136/heartjnl-2017-312107}.  	 	 		\textit{British Medical Journal}  		2019. 0 p. .  	 
\bibitem[Sari et al. ()]{b31}\label{b31} 	 		‘Cigarette Smoking and Hyperglycaemiain Diabetic Patients’.  		 			M I Sari 		,  		 			N Sari 		,  		 			D M Darlan 		,  		 			R J Prasetya 		.  	 	 		\textit{Open Access Macedonian Journal of Medical Sciences}  		2018. 140 p. .  	 
\bibitem[Virdis et al. ()]{b35}\label{b35} 	 		‘Cigarette Smoking and Hypertension’.  		 			A Virdis 		,  		 			C Giannarelli 		,  		 			M Fritsch Neves 		,  		 			S Taddei 		,  		 			L Ghiadoni 		.  		 \xref{http://dx.doi.org/10.2174/138161210792062}{10.2174/138161210792062}.  	 	 		\textit{Current Pharmaceutical Design}  		2010. 16  (23)  p. .  	 
\bibitem[Hartz et al. ()]{b19}\label{b19} 	 		‘Comorbidity of Severe Psychotic Disorders with Measures of Substance Use’.  		 			S M Hartz 		,  		 			C N Pato 		,  		 			H Medeiros 		,  		 			P Cavazos-Rehg 		,  		 			J L Sobell 		,  		 			J A Knowles 		,  		 			M T Pato 		.  		 \xref{http://dx.doi.org/10.1001/jamapsychiatry.2013.3726}{10.1001/jamapsychiatry.2013.3726}.  	 	 		\textit{JAMA Psychiatry}  		2014. 71  (3)  p. 248.  	 
\bibitem[Suetani et al. ()]{b22}\label{b22} 	 		‘Correlates of physical activity in people living with psychotic illness’.  		 			S Suetani 		,  		 			A Waterreus 		,  		 			V Morgan 		,  		 			D L Foley 		,  		 			C Galletly 		,  		 			J C Badcock 		,  		 			J J Mcgrath 		.  		 \xref{http://dx.doi.org/10.1111/acps.12594}{10.1111/acps.12594}.  	 	 		\textit{Acta Psychiatrica Scandinavica}  		2016. 134  (2)  p. .  	 
\bibitem[Trinder ()]{b13}\label{b13} 	 		‘Determination of glucose in blood using glucose oxidase with an alternative acceptor’.  		 			P Trinder 		.  	 	 		\textit{Ann. of clin. Biochem}  		1959. 6 p. .  	 
\bibitem[Clair et al. ()]{b29}\label{b29} 	 		‘Dose-dependent positive association between cigarette smoking, abdominal obesity and body fat: cross-sectional data from a populationbased survey’.  		 			C Clair 		,  		 			A Chiolero 		,  		 			D Faeh 		,  		 			J Cornuz 		,  		 			P Marques-Vidal 		,  		 			F Paccaud 		,  		 			P Vollenweider 		.  		 \xref{http://dx.doi.org/10.1186/1471-2458-11-23}{10.1186/1471-2458-11-23}.  	 	 		\textit{BMC Public Health}  		2011.  (1)  p. 11.  	 
\bibitem[Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Expert Panel on Detection ()]{b15}\label{b15} 	 		‘Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III)’.  		 \xref{http://dx.doi.org/10.1001/jama.285.19.2486}{10.1001/jama.285.19.2486}.  		 \url{https://doi.org/10.1001/jama.285.19.2486}  	 	 		\textit{Expert Panel on Detection}  		2001. 285 p. .  	 	 (JAMA) 
\bibitem[Tianyi et al. ()]{b12}\label{b12} 	 		‘Factors Associated with the Prevalence of Cognitive Impairment in a Rural Elderly Cameroonian Population: A Community-Based Study in Sub-Saharan Africa’.  		 			F L Tianyi 		,  		 			V N Agbor 		,  		 			A K Njamnshi 		,  		 			J Atashili 		.  	 	 		\textit{Dementia and Geriatric Cognitive Disorders}  		2019. p. .  	 
\bibitem[Hahn et al. ()]{b20}\label{b20} 	 		‘Inadequate fruit and vegetable intake in people with psychosis’.  		 			L A Hahn 		,  		 			C A Galletly 		,  		 			D L Foley 		,  		 			A Mackinnon 		,  		 			G F Watts 		,  		 			D J Castle 		,  		 			V A Morgan 		.  		 \xref{http://dx.doi.org/10.1177/0004867414553950}{10.1177/0004867414553950}.  	 	 		\textit{Australian \& New Zealand Journal of Psychiatry}  		2014. 48  (11)  p. .  	 
\bibitem[Freyberg et al. ()]{b6}\label{b6} 	 		‘Intrinsic and Antipsychotic Drug-Induced Metabolic Dysfunction in Schizophrenia’.  		 			Z Freyberg 		,  		 			D Aslanoglou 		,  		 			R Shah 		,  		 			J S Ballon 		.  		 \xref{http://dx.doi.org/10.3389/fnins.2017.00432}{10.3389/fnins.2017.00432}.  	 	 		\textit{Frontiers in Neuroscience}  		2017. p. 11.  	 
\bibitem[Nyboe and Lund ()]{b17}\label{b17} 	 		‘Low levels of physical activity in patients with severe mental illness’.  		 			L Nyboe 		,  		 			H Lund 		.  		 \xref{http://dx.doi.org/10.3109/08039488.2012.675588}{10.3109/08039488.2012.675588}.  	 	 		\textit{Nordic Journal of Psychiatry}  		2012. 67  (1)  p. .  	 
\bibitem[Samaan and Amira ()]{b32}\label{b32} 	 		‘Obesity, insulin resistance and type 2 diabetes: the interaction between fat cells and muscle cells’.  		 			C Samaan 		,  		 			K P Amira 		.  	 	 		\textit{Proceedings of scientific conferences of the Endocrinology and Metabolism Directorate},  				 (scientific conferences of the Endocrinology and Metabolism Directorate)  		2008. 8.  	 
\bibitem[Obesity, Prevention and Management of World Epidemy World Health Organization ()]{b14}\label{b14} 	 		‘Obesity, Prevention and Management of World Epidemy’.  	 	 		\textit{World Health Organization}  		2003.  (916) .  	 	 (Technical Reports Series) 	 (World Health Organization) 
\bibitem[Sharma et al. ()]{b23}\label{b23} 	 		‘Paradoxical Effects of Fruit on Obesity’.  		 			S P Sharma 		,  		 			H J Chung 		,  		 			H J Kim 		,  		 			S T Hong 		.  		 \xref{http://dx.doi.org/10.3390/nu8100633}{10.3390/nu8100633}.  	 	 		\textit{Nutrients}  		2016. 633  (8)  p. .  	 
\bibitem[Vancampfort et al. ()]{b11}\label{b11} 	 		‘Prevalence of metabolic syndromeand metabolic abnormalities in schizophrenia and related disorders-a systematic 12’.  		 			Mitchell A J Vancampfort 		,  		 			D Sweers 		,  		 			K R Winkelv 		,  		 			De Yuw 		,  		 			M Hert 		.  		 \xref{http://dx.doi.org/10.1155/2014/195750}{10.1155/2014/195750}.  	 	 		\textit{International Journal of Alzheimer's disease}  		Olayinka, O. O., \& Mbuyi, N. N. (ed.)  		2013. 2014. 2014. p. .  	 	 (Epidemiology of Dementia among the Elderly in Sub-Saharan Africa) 
\bibitem[Silverstone et al. ()]{b24}\label{b24} 	 		‘Prevalence of Obesity in Patients Receiving Depot Antipsychotics’.  		 			T Silverstone 		,  		 			G Smith 		,  		 			E Goodall 		.  		 \xref{http://dx.doi.org/10.1192/bjp.153.2.214}{10.1192/bjp.153.2.214}.  	 	 		\textit{British Journal of Psychiatry}  		1988. 153  (02)  p. .  	 
\bibitem[Mavrodaris et al. ()]{b33}\label{b33} 	 		‘Prevalences of dementia and cognitive impairment among older people in sub-Saharan Africa: a systematic review’.  		 			A Mavrodaris 		,  		 			J Powell 		,  		 			M Thorogood 		.  		 \xref{http://dx.doi.org/10.2471/blt.13.118422}{10.2471/blt.13.118422}.  	 	 		\textit{Bulletin of the World Health Organization}  		2013. 91  (10)  p. .  	 
\bibitem[Sicras-Mainar et al. ()]{b25}\label{b25} 	 		‘Relationship between obesity and antipsychotic drug use in the adult population: A longitudinal, retrospective claim database study in Primary Care settings’.  		 			A Sicras-Mainar 		,  		 			R Navarro-Artieda 		,  		 			J Rejas-Gutiérrez 		,  		 			M Blanca-Tamayo 		.  	 	 		\textit{Neuropsychiatric Disease and Treatment}  		2008. 4  (1)  p. .  	 
\bibitem[Schröder et al. ()]{b30}\label{b30} 	 		‘Relationship of abdominal obesity with alcohol consumption at population scale’.  		 			H Schröder 		,  		 			J A Morales-Molina 		,  		 			S Bermejo 		,  		 			D Barral 		,  		 			E S Mándoli 		,  		 			M Grau 		,  		 			J Arrugat 		.  		 doi:10.1007 /s00394-007-0674-7.  	 	 		\textit{European Journal of Nutrition}  		2007. 46  (7)  p. .  	 
\bibitem[Schizophrenia patients at higher risk of diabetes, hypertension, and hyperlipidemia: A populationbased study Schizophrenia Research]{b10}\label{b10} 	 		‘Schizophrenia patients at higher risk of diabetes, hypertension, and hyperlipidemia: A populationbased study’.  		 \xref{http://dx.doi.org/10.1016/j.schres.2010.12.007}{10.1016/j.schres.2010.12.007}.  	 	 		\textit{Schizophrenia Research}  		126  (1-3)  p. .  	 
\bibitem[Vancampfort et al. ()]{b16}\label{b16} 	 		‘Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis’.  		 			D Vancampfort 		,  		 			J Firth 		,  		 			F B Schuch 		,  		 			S Rosenbaum 		,  		 			J Mugisha 		,  		 			M Hallgren 		,  		 			B Stubbs 		.  		 \xref{http://dx.doi.org/10.1002/wps.20458}{10.1002/wps.20458}.  	 	 		\textit{World Psychiatry}  		2017. 16  (3)  p. .  	 
\bibitem[Aubin et al. ()]{b18}\label{b18} 	 		‘Smoking, quitting, and psychiatric disease: a review’.  		 			H J Aubin 		,  		 			H Rollema 		,  		 			T H Svensson 		,  		 			G Winterer 		.  	 	 		\textit{Neuroscience and Biobehavioral Reviews}  		2012. 36  (1)  p. .  	 
\bibitem[Dipasquale et al. ()]{b5}\label{b5} 	 		‘The dietary pattern of patients with schizophrenia: A systematic review’.  		 			S Dipasquale 		,  		 			C M Pariante 		,  		 			P Dazzan 		,  		 			E Aguglia 		,  		 			P Mcguire 		,  		 			V Mondelli 		.  		 \xref{http://dx.doi.org/10.1016/j.jpsychires.2012.10.005}{10.1016/j.jpsychires.2012.10.005}.  	 	 		\textit{Journal of Psychiatric Research}  		2013. 47  (2)  p. .  	 
\bibitem[Bruins et al. ()]{b7}\label{b7} 	 		‘The Effects of Lifestyle Interventions on (Long-Term) Weight Management, Cardiometabolic Risk and Depressive Symptoms in People with Psychotic Disorders: A Meta-Analysis’.  		 			J Bruins 		,  		 			F Jörg 		,  		 			R Bruggeman 		,  		 			C Slooff 		,  		 			E Corpeleijn 		,  		 			M Pijnenborg 		.  	 	 		\textit{PLoS ONE}  		2014. 9  (12)  p. e112276.  	 
\bibitem[Brown et al. ()]{b21}\label{b21} 	 		‘The unhealthy lifestyle of people with schizophrenia’.  		 			S Brown 		,  		 			J Birtwistle 		,  		 			L Roe 		,  		 			C Thompson 		.  	 	 		\textit{Psychol Med}  		1999. 29  (3)  p. .  	 
\bibitem[Alves et al. ()]{b36}\label{b36} 	 		‘Use of atypical antipsychotics and risk of hypertension: A case report and review literature’.  		 			B B Alves 		,  		 			G D P Oliveira 		,  		 			M G M Neto 		,  		 			R B Fiorilli 		,  		 			E D E S Cestário 		.  	 	 		\textit{SAGE Open Medical Case Reports}  		2019. 7 p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
