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 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 5  |  Issue : 2  |  Page : 37-44

Impaired cognition and normal cardiometabolic parameters in patients with type 2 diabetes in Kano, Nigeria


Department of Human Physiology, Bayero University Kano, Kano, Nigeria

Date of Web Publication1-Nov-2018

Correspondence Address:
Isyaku G Mukhtar
Department of Human Physiology, Bayero University Kano, Kano
Nigeria
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DOI: 10.4103/ssajm.ssajm_19_18

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  Abstract 

Background: Type 2 diabetes (T2D) has been linked with impaired cognition, elevated blood pressure, and dyslipidemia. However, these findings have not been uniform.
Aim: This study aimed to assess cognition and its relation with fasting blood sugar (FBS), HbA1c, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and serum levels of triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol in T2D patients.
Materials and Methods: Thirty-four T2D patients attending the diabetic clinic of Murtala Mohammed Specialist Hospital, Kano, between June and December 2017 and age, sex, and level of education matched controls were recruited for the study. All participants were screened for depression using Beck’s depression inventory II. Cognitive function was assessed using Montreal Cognitive Assessment test (MoCA) version 7.3. Blood pressure was measured using mercury sphygmomanometer (Dekamet Accoson®, Harlow, Essex, UK) and Littman’s stethoscope. FBS was measured using glucometer (On Call® Plus; Alan Lab. Inc., San Diego, California, USA) after an overnight fast. HbA1c was determined using ion exchange chromatography and serum triglycerides, total cholesterol, and HDL cholesterol were determined spectrophotometrically. Data were analyzed on Statistical Package for Social Scientists version 23.0. P value ≤0.05 was considered significant.
Results: Diabetic group had significantly lower MoCA score (U = 216.50, P = 0.001) compared to the controls (19.5 and 26.0, respectively). MoCA score was influenced by sex (U = 88.0, P = 0.05), level of education (X2 = 12.826, P = 0.005), and;Deg;BM;Deg;I (X2 = 8.529, P = 0.036) among diabetic patients. MoCA score was correlated with level of education of the diabetic patients (X2 = 14.664, P = 0.002). Duration of diabetes, FBS, and HbA1c had no relationship with MoCA score. The diabetic patients had statistically (U = 416.50, P = 0.048) lower serum triglycerides compared to the control group (132.5 and 155.5 mg/dl, respectively) even though both were within normal limits. Serum HDL (16.65 and 16.80 mg/dl, respectively), LDL (123.2 and 115.7 mg/dl, respectively), total cholesterol (165.20 and 164.0 mg/dl, respectively),;Deg;SB;Deg;P, DBP,;Deg;MA;Deg;P, and;Deg;BM;Deg;I were not significantly different between the two groups and were within normal limits.
Conclusion: The diabetics had impaired cognition and normal cardiometabolic parameters. Routine screening for mild cognitive impairment should be employed in the management of T2D.

Keywords: Cognition, impairment, Kano, T2D


How to cite this article:
Yarube IU, Mukhtar IG. Impaired cognition and normal cardiometabolic parameters in patients with type 2 diabetes in Kano, Nigeria. Sub-Saharan Afr J Med 2018;5:37-44

How to cite this URL:
Yarube IU, Mukhtar IG. Impaired cognition and normal cardiometabolic parameters in patients with type 2 diabetes in Kano, Nigeria. Sub-Saharan Afr J Med [serial online] 2018 [cited 2018 Nov 19];5:37-44. Available from: http://www.ssajm.org/text.asp?2018/5/2/37/243937


  Introduction Top


The effect(s) of type 2 diabetes (T2D) on central nervous system have been the subject of many systematic reviews and original articles over the years.[1] Despite large volume of data generated from various studies on this topic, the results are often conflicting.[1],[2] Despite this, T2D is linked to decline in cognitive function, dementia, and Alzheimer’s disease, especially in the elderly.[3] Manschot et al.[4] concluded that the relationship between cognitive impairment in T2D and structural brain changes is complex and still unclear.

Literature on the effect(s) of T2D on cognition is full of inconsistencies, largely because of differences in study design, neuropsychiatric test battery used, study population, and other covariates.[2],[5]

To our knowledge, there have not been many studies on cognitive function among T2D using Montreal Cognitive Assessment test (MoCA) in this environment.

The aim of the study was to assess cognition, serum triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, fasting blood sugar (FBS), glycated hemoglobin, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial blood pressure (MAP), body mass index (BMI), and sociodemographic characteristics among type 2 diabetic patients and their nondiabetic controls.


  Materials and methods Top


Study area

The study was conducted at the diabetic clinic of Murtala Muhammad Specialist Hospital, Kano, Nigeria, between June and December 2017.

Ethical consideration

Ethical approval was obtained from Kano State Ministry of Health, and all participants were requested to sign an individual informed consent form. All procedures were in accordance with Helsinki Declaration of 1975, as revised in 2000.

Sample size determination

Sample size was determined using computer software for power and sample size determination according to Lenth[6] after a pilot study which gave a minimum sample size of 32.

Study design and sampling technique

The study design was descriptive, analytical study. Systematic sampling technique was used to recruit patients.

Inclusion criteria

  1. All male and female patients with T2D less than 65 years of age who signed written informed consent for the study.
  2. Healthy nondiabetic patients matched for age, sex, and level of education were included as controls.


Exclusion criteria

  1. Patients of 65 years and above.
  2. Patients with history or evidence of neuropsychiatric illness.
  3. Patients who declined to sign consent form.
  4. History or evidence of recent smoking or alcohol usage within the last 2 years.
  5. Patients with evidence of on-going depression.


Data collection

A data capture form was used to obtain sociodemographic and clinical information of the participants.

Blood pressure was measured using sphygmomanometer (Dekamet MSK Ltd., England) and Littmann’s stethoscope. Patients were asked to seat comfortably, rested for 5 min, and reading taken from right arm. The cuff was applied to the right arm with the arrow mark pointing to the direction of brachial artery. The cuff was then inflated while radial pulse was palpated at the same time. The sphygmomanometer was inflated 10 mmHg above the point at which radial pulse disappeared. The stethoscope was then placed on the antecubital fossa and the cuff deflated gradually. The point at which the Korotkoff’s sound appeared was taken as systolic pressure, whereas its disappearance was taken as diastolic pressure. MAP was calculated from the following relation:



where pulse pressure is the difference between SBP and DBP.

Height and weight were measured using stadiometer (Echukson®, Hamason, China). Patients were asked to remove their shoes, caps, empty their pockets, and stand erect with the head facing directly forward. BMI was then calculated for each patient as weight in kg divided by height in meter square.

All the patients were screened for depression using Beck’s depression inventory II (BDI II). And patients with BDI II score of 19 and above were excluded from the study.[7]

MoCA version 7.3 was used to assess cognitive function among diabetic and nondiabetic patients. A score of 26 and above was considered normal, whereas a score of less than 26 was considered impaired cognition. The test was administered according the author’s guidelines and instructions.[8]

Laboratory procedures

Samples for determination of FBS, glycated hemoglobin, and serum lipids were collected between 7 and 8 am after an overnight fast. Plain sample bottles were used for all the parameters, except samples for glycated hemoglobin for which Ethylenediaminetetraacetic acid (EDTA) bottles were used. Serum was extracted from each sample after allowing it to clot at room temperature and then centrifuged at 2400 g for 5 min and stored at −20°C until analysis.

Glycated hemoglobin was determined using ion exchange chromatography as described by Pecoraro et al.[9] Blood sample was allowed to undergo hemolysis and then continuously mixed with ion exchange resin for 5 min. The nonglycated hemoglobin binds to the ion exchange resin during mixing leaving the glycated hemoglobin (GHb) free in the supernatant. A filter separator was used to remove the resin from the supernatant. Absorbance of the glycated hemoglobin (GHb) and that of total hemoglobin fraction (THb) were measured. The ratio of the absorbance of GHb to the absorbance of THb of the control and test were used to calculate the percent GHb of the samples.

Fasting blood glucose was determined using glucometer (On Call® Plus; Alan Lab. Inc., San Diego, California, USA). The finger tip of each patient was cleaned with methylated spirit, allowed to dry, and pricked using a sterile lancet. A drop of capillary blood was then placed on the glucometer, and the result was read from the screen.

Serum triglyceride, HDL, and total cholesterol were determined using enzymatic methods as described by Sagiura et al.,[10] Steele et al.,[11] and Abell et al.,[12] respectively. It involves enzymatic hydrolysis of triglycerides, total cholesterol, and HDL by specific reagents as appropriate. Absorbances of the resulting colored compounds were then measured using spectrophotometer (721-VIS Spectrophotometer; Yangzhou Wandong Medical Co., Ltd., Yangzhou, China) at wavelength of 560 nm. Actual concentration of each parameter was then manually calculated from the absorbance and concentration of the standard. LDL cholesterol was calculated from the following relation:



Statistical analysis

Data were analyzed using Statistical Package for Social Scientists version 23.0 (International Business Machines Corporation. IBM SPSS Statistics for Windows, Version 23.0, Armonk, NY, IBM Corp., 2015)[13] and expressed as median, frequencies, and percentages. Nonparametric methods were used in the analysis following normality tests. Mann–Whitney U and Kruskal–Wallis tests were used to compare medians and proportions between diabetic and nondiabetic groups. Chi-square test of association was used to determine association between qualitative variables. The significance of Spearman’s correlation coefficient was determined to assess association between cognitive function and serum triglycerides, total cholesterol, HDL, LDL, SBP, DBP, MAP, FBS, HbA1c, and BMI. P values ≤0.05 were considered statistically significant.


  Results Top


Normality test

Majority of the characteristics in both diabetic and nondiabetic groups were not normally distributed (P < 0.05). This informed the use of nonparametric statistics to analyze the data—[Table 1].
Table 1 Normality tests for participants’ clinico-laboratory characteristics

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Sociodemographic characteristics of the participants

Majority (>80%) of the participants in both diabetic and nondiabetic groups fell within the age range of 46 to 65 years. There was no significant difference in the age of the participants between the two groups (U = 574.00, P = 0.961). The study groups comprised 17 male and 17 female participants each (U = 578.00, P = 1.00)—[Table 2].
Table 2 Sociodemographic characteristics of the participants

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Clinical characteristics of the participants

The diabetic patients had a median MoCA score of 19.50 which implies impaired cognition; the controls had median MoCA score of 26.00, indicating normal cognitive function. The difference in MoCA score of the two groups was statistically significant (U = 216.50, P = 0.001).

[Table 3] contains the details of other clinical characteristics of the participants. About 59% of the diabetics had normal or below normal BMI, 91% had poor glycemic control, and up to 56% of them have lived with the disease for more than 6 years.
Table 3 Clinical characteristics of the participants

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Laboratory characteristics of the participants

The median value of serum triglycerides among the diabetic group was statistically (U = 416.50, P = 0.048) lower compared to the nondiabetic controls (132.5 and 155.5 mg/dl, respectively). However, both values for the diabetic and nondiabetic groups were within normal limits (61–156 mg/dl).[14] There was no significant difference in serum HDL (U = 571.50, P = 0.936), LDL (U = 572.00, P = 0.941), and total cholesterol (U = 548.00, P = 0.713) between the diabetic and nondiabetic groups. The reported values fall within normal range.[14] This indicates normal lipid metabolism in both groups.

There was no significant difference in BMI between the diabetics and nondiabetic controls (U = 431.00, P = 0.071), and the median values (23.39 and 21.33 kg/m2, respectively) fall within the normal range of 18.5 to 25 kg/m2.

FBS was significantly (U = 39.50, P = 0.001) higher among diabetic patients (8.8 mmol/l) compared to nondiabetic controls (4.75 mmol/l). With the normal range of fasting plasma sugar being 3.5 to 6.5 mmol/l,[15] this result indicates poor glycemic control among the diabetic group and normal glucose metabolism among the nondiabetic group.

Glycated hemoglobin was also significantly (U = 3.26, P = 0.020) higher among the diabetic group (13.68%) compared to the nondiabetic group (11.79%), but both values were higher than normal. This indicates that both diabetics and their nondiabetic controls had abnormal glucose control over the last 3 months.

There was no significant difference in SBP, DBP, and MAP between the diabetic and nondiabetic groups (U = 493.00, P = 0.288; U = 541.00, P = 0.622; and U = 516.00, P = 0.448, respectively). Median values for all laboratory parameters are present in [Table 4].
Table 4 Laboratory characteristics of the diabetic and nondiabetic subjects

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Variation of MoCA score according to the characteristics of the participants

There was significant variation in MoCA score according to BMI among the diabetics (X2 = 8.529, P = 0.036) [Table 5]. Those with the lowest BMI (≤18.5 kg/m2) had the highest MoCA score of 22.5, whereas those with the highest BMI (≥30 kg/m2) had the lowest MoCA score of 18. Similarly, there was no variation in MoCA score according to MAP, HDL, LDL, total cholesterol, triglycerides, FBS, and HbA1c in the diabetic group. Variation of MoCA score according to SBP and DBP could not be calculated because the scores fell in the same category.
Table 5 Variations of MoCA score according to sociodemographic and cardiometabolic parameters of the diabetic patients

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MoCA score also significantly varied according to sex in the diabetic (P = 0.05) group, with male diabetics having a score of 20 and females having a score of 19. Furthermore, MoCA score also varied according to level of education among the diabetics (P = 0.005). Among the diabetics, those with secondary education had the highest score of 20, followed by those with primary and nonformal education with 21 and 18, respectively.

Relationship of MoCA score with characteristics of the diabetic patients

There was significant association between MoCA score and level of education of the diabetic patients (X2 = 14.664, P = 0.002). Further, there was significant negative correlation between MoCA score and serum levels of triglycerides (r = −0.511, P = 0.002), indicating that MoCA score increased as serum triglyceride level decreases—[Table 6].
Table 6 Relationship of MoCA score with characteristics of diabetic patients

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  Discussion Top


This study demonstrated impaired cognition in type 2 diabetic patients. Diabetes mellitus has been linked with development of cognitive impairment by various mechanisms.[16],[17],[18],[19] At the heart of these mechanisms is sustained hyperglycemia.[20] Hyperglycemia, through diversion of excess glucose to polyol pathway, leads to depletion of nicotinamide adenine dinucleotide phosphate (NADPH) which is required for regeneration of antioxidant enzymes and hence leading to production of free radicals.[21] Hyperglycemia also causes nonenzymatic glycation of proteins leading to generation of advanced glycation end products and overflow of electrons from mitochondria in the electron transport chain.[1] All these processes lead to increased production of reactive oxygen and nitrogen species with subsequent micro- and macrovascular damages, especially in the brain and hence decline in cognitive function.[22] The finding of this study on impaired cognition agrees with previous researchers who reported lower cognitive function score among type 2 diabetic patients compared to nondiabetic controls.[3],[19],[23],[24],[25] However, although about 90% of diabetic patients had impaired cognitive function, about two-third of the nondiabetic control patients also had the same condition. This implies that, apart from diabetes, other factors could account for the decline in cognitive function observed. Age has been reported to be associated with progressive decline in cognitive function.[3],[26] Over half of the participants in both diabetic and nondiabetic groups had no formal education. Formal education is likely to provide for better socioeconomic status, improved quality of life, and better social interactions. All these are associated with better cognitive development and slow rate of decline in cognitive function.[3],[27],[28]

The significant variation of cognition with BMI could be as a result of the complex interplay among obesity, dyslipidemia, and insulin resistance.[29] Central nervous system insulin resistance has been reported to have influence over cognitive function due to insulin’s role in learning, executive function, and memory formation.[30]

The variation of cognition according to sex with male diabetics having higher MoCA score could be due to the fact that women in this part of the world are more likely to be less formally educated, poorly empowered economically, and more likely to live without a partner at old age and hence without emotional and social support.[31]

The lack of relationship between cognitive function and duration of diabetes is in agreement with a previous study among Ethiopian population which demonstrated no association between cognitive impairment and duration of diabetes.[32] However, our results disagree with others that reported negative relationship between cognition and duration of diabetes.[17],[23]

We found no significant association between cognitive function score and FBS and glycated hemoglobin. This is similar to what was reported by Yusuf et al.[3] However, other researchers reported strong association between measures of glycemic control and cognitive function scores.[28] Perhaps these inconsistencies can be explained by the findings of Lee and Kwon[19] who postulated that it is the fluctuation in blood sugar rather than the absolute value of blood sugar at any given time that causes the macrovascular events leading to cognitive impairment.

The normal serum lipid profile found in this study could be due to the influence of well-structured health education programs provided by the Diabetes Association of Nigeria at the clinics on weekly basis. The dietary education and the receptive nature of the diabetic patients to such advices may also be a reason for the nearly normal lipid profile. Low socioeconomic and formal education status might have prohibited consumption of highly refined diet with high fat content, as the study hospital is frequented by those in lower socioeconomic class. Although formal education provides a means for better socioeconomic status, it also predisposes to easy access to junk and unhealthy diet. Other researchers reported similar findings of normal lipid profile among diabetic patients.[33]

Despite the reported link between dyslipidemia and cognitive impairment in diabetic patients through endothelial injury and alteration in fibrinolytic systems Kumari et al.,[34] we found no correlation between any of the lipid profile parameters with cognitive function.

Persistent hyperglycemia, the hallmark of T2D, is an important initiator of most of the processes leading to cognitive impairment in type 2 diabetic patients.[35] The finding of this study in terms of FBS is in agreement with a number of previous studies.[36],[37] Glycated hemoglobin is formed as a result of glycation of hemoglobin by glucose at N-terminal amino acid valine on the β chain.[38] It is used to assess glycemic control over the past 2 to 3 months and is said to be unaffected by the daily glucose fluctuations.[15] However, this study noted an abnormally high HbA1c among the nondiabetic patients that fall within the diabetic range. Various factors like method of analysis, hemoglobin variants, presence of various medical conditions like chronic kidney disease have been reported to increase glycated hemoglobin values.[38] The prevalence of hemoglobinopathies been high in this environment Bello et al.[39] and the fact that we did not screen the patients for hemoglobinopathies could have accounted for the higher percentage glycated hemoglobin among the controls. Iron deficiency anemia, B12 deficiency anemia, hypertriglyceridemia, hyperbilirubinemia, and uremia are all associated with elevated HbA1c. Ion exchange chromatography was used in the determination of HbA1c in this study as against high-performance liquid chromatography (HPLC). It is said to be less accurate than the gold standard HPLC and might have contributed to the abnormally high HbA1c levels among the nondiabetics. This, and our inability to screen the patients for hemoglobinopathies, is a part of the limitations of this study. Some of the patients in the nondiabetic group might also be in the prediabetic state or having an overt diabetes that was not diagnosed prior to this study. Indeed, about 15% of the nondiabetic patients in this study had FBS in the diabetic range. None of these markers of glycemic control correlated with cognitive function score.The findings of this study in SBP, DBP, and MAP are in agreement with what was reported by Chen et al.[23] Diabetes increases the risk of development of elevated blood pressure by a number of mechanisms. Hyperglycemia, by causing glomerular hyperfiltration, increases renal sodium retention and hence extracellular volume.[40] Persistent hyperglycemia in diabetic patients also causes toxic injury to vascular endothelium, thereby increasing vascular reactivity and atherosclerotic plugs formation.[41] The normal SBP, DBP, and MAP among the diabetic group in this study could be due to lifestyle modification, physical exercise, healthy dieting, and the normal lipid profile noted earlier in the study patients. A number of the diabetic patients, even though hypertensive, were well controlled on antihypertensives.


  Conclusion Top


The diabetics had mild cognitive impairment with MoCA score of 19.50, whereas the nondiabetic controls showed normal cognitive function with score of 26 on the same test. Cognitive impairment was influenced by sex, education, and BMI, and had significant relationship with level of education and serum levels of triglycerides. The patients also had cardiometabolic parameters including BMI, MAP, HDL, LDL, total cholesterol, and triglycerides within normal limits.

ACKNOWLEDGMENT

We acknowledge the Directorate of Research, Innovation, and Partnership of Bayero University Kano for a research grant for this work.

Financial support and sponsorship

We received a grant from the Directorate of Research, Innovation, and Partnership of Bayero University, Kano with grant no. BUK/DRIP/RG/2017/0024 for this work.

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Reijmer YD, Berg EV, Ruis C. Cognitive dysfunction in patients with type 2 diabetes. Diabetes Metab Res Rev 2010;26:507-19.  Back to cited text no. 1
    
2.
Fischer AL, de Frias CM, Yeung SE, Dixon RA. Short-term longitudinal trends in cognitive performance in older adults with type 2 diabetes. J Clin Exp Neuropsychol 2009;31:809-22.  Back to cited text no. 2
    
3.
Yusuf AJ, Baiyewu O, Bakari AG, Garko SB, Jibril ME, Suleiman AM et al. Low education and lack of spousal relationship are associated with dementia in older adults with diabetes mellitus in Nigeria. Psychogeriatric 2018;18:216-23.  Back to cited text no. 3
    
4.
Manschot SM, Brands AMA, Grond JVD, Kessels RPC, Algra A, Kappelle LJ. Brain magnetic resonance imaging correlates of impaired cognition in patients with type 2 diabetes. Diabetes 2006;55:1106-13.  Back to cited text no. 4
    
5.
Berg EVD, Reijmer YD, Bresser JD. A 4 year follow-up study of cognitive functioning in patients with type 2 diabetes mellitus. Diabetologia 2010;53:58-65.  Back to cited text no. 5
    
6.
Lenth RV. Java applets for power and sample size [computer software]. Iowa: Iowa University; 2009. Retrieved from: homepage.stat.uiowa.edu. [Last accessed on June 14, 2017]. 2100 GMT.  Back to cited text no. 6
    
7.
Beck ATS, Robert AB, Gregory K. BDI-II, beck depression inventory: Manual. San Antonio: Tex Psychological Corp 1996.  Back to cited text no. 7
    
8.
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695-9.  Back to cited text no. 8
    
9.
Pecoraro RE, Graf RJ, Holter JB, Beiter H, Porte JD. Comparison of a colorimetric assay for glycated haemoglobin with ion-exchange chromatography. Diabetes 1979;28:1120-5.  Back to cited text no. 9
    
10.
Sagiura M, Oikawa T, Hirano K, Maeda H, Yoshimora H, Sugiyama M et al. A simple colorimetric method for determination of serum triglycerides with lipoprotein lipase and glycerol dehydrogenase. Clin Chim Acta 1977;81:125-30.  Back to cited text no. 10
    
11.
Steele BW, Koehler DF, Kuba K, Azar M. An enzymatic approach to lipoprotein quantification. Am J Clin Pathol 1980;73:75-8.  Back to cited text no. 11
    
12.
Abell LL, Levy BB, Brodie BB, Kendall FE. A simplified method for the estimation of total cholesterol in serum and demonstration of its specificity. J Biol Chem 1952;195:356-66.  Back to cited text no. 12
    
13.
International Business Machines Corporation. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp.; 2015.  Back to cited text no. 13
    
14.
Alemnji GA, Mbuagbaw J, Folefac E, Teto G, Nkengafac S, Atems N et al. Reference physiological ranges for serum biochemical parameters among healthy Cameroonians to support HIV vaccine and related clinical trials. Afr J Health Sci 2010;17:75-82.  Back to cited text no. 14
    
15.
American Diabetic Association. Classification and diagnosis of diabetes. Diabetes Care 2015;38:S8-16.  Back to cited text no. 15
    
16.
Kalar MU, Mujeeb E, Pervez S, Lalani Z, Raza B, Batool A et al. Assessment of cognitive status in type 2 diabetes. Int J Collab Res Intern Med Public Health 2014;6:235-2.  Back to cited text no. 16
    
17.
West RK, Ravona-Springer R, Schmeidler J, Leroith D, Koifman K, Guerrero-Berroa E et al. The association of duration of type 2 diabetes with cognitive performance is modulated by long-term glycemic control. Am J Geriatr Psychiatry 2014;22:1055-9.  Back to cited text no. 17
    
18.
Erus G, Battapady H, Zhang T, Lovato J, Miller ME, Williamson JD et al. Spatial patterns of structural brain changes in type 2 diabetic patients and their longitudinal progression with intensive control of blood glucose. Diabetes Care 2015;38:97-104.  Back to cited text no. 18
    
19.
Lee K, Kwon JC. The fluctuation index of blood glucose level is an important factor for predicting cognitive decline in diabetic patients with cognitive impairment: 1-Year prospective, observational study. Alzheimers Dement 2017;13:P265.  Back to cited text no. 19
    
20.
Barbieri M, Boccardi V, Paolisso G. Cognitive decline and diabetes; a focus on linking mechanisms. In: Martin CR, Preedy VR, editors. Diet and nutrition in dementia and cognitive decline. San Diego, USA: Elsevier Science Publishing Co Inc; 2015. pp 393–402.  Back to cited text no. 20
    
21.
Frisardi V, Solfrizzi V, Seripa D, Capurso C, Santamato A, Sancarlo D et al. Metabolic-cognitive syndrome: A cross-talk between metabolic syndrome and Alzheimer’s disease. Ageing Res Rev 2010;9:399-417.  Back to cited text no. 21
    
22.
Moran C, Phan TG, Chen J, Blizzard L, Beare R, Venn A et al. Brain atrophy in type 2 diabetes regional distribution and influence on cognition. Diabetes Care 2013;36:4036-42.  Back to cited text no. 22
    
23.
Chen R, Jiang X, Zhao X, Qin Y, Gu Z, Gu P et al. Risk factors of mild cognitive impairment in middle aged patients with type 2 diabetes: A cross-sectional study. Ann Endocrinol (Paris) 2012;73:208-12.  Back to cited text no. 23
    
24.
Moore EM, Mander AG, Ames D, Kotowicz MA, Carne RP, Brodaty H et al. Increased risk of cognitive impairment in patients with diabetes is associated with metformin. Diabetes Care 2013;36:2981-7.  Back to cited text no. 24
    
25.
Hazari MAH, Ram RB, Uzma N, Santhosh KB. Cognitive impairment in type 2 diabetes mellitus. Int J Diabetes Mellit 2015;3:19-24.  Back to cited text no. 25
    
26.
Sachdev PS, Lipnick DM, Crawford J, Reppermund S, Kochan NA, Trollor JN et al. Risk profiles for mild cognitive impairment vary by age and sex: The Sydney memory and ageing study. Am J Geriatr Psychiatry 2012;20:854-65.  Back to cited text no. 26
    
27.
Maria SDB, Nitrini R. Cognitive impairment in individuals with low educational level and homogeneous sociocultural background. Dement Neuropsychol 2014;8:345-50.  Back to cited text no. 27
    
28.
Gorska-Ciebiada M, Saryusz-Wolska M, Ciebiada M, Loba J. Mild cognitive impairment and depressive symptoms in elderly patients with diabetes: Prevalence, risk factors, and comorbidity. J Diabetes Res 2014;2014:179-86.  Back to cited text no. 28
    
29.
Sandip KD. Cognitive impairment and diabetes. Recent Pat Endocr Metab Immune Drug Discov 2013;7:1-11.  Back to cited text no. 29
    
30.
Banks WA, Owen JB, Erickson MA. Insulin in the brain: There and back again. Pharmacol Ther 2012;136:82-93.  Back to cited text no. 30
    
31.
Onadja Y, Atchess N, Sour BA, Rossier C, Zunzunegui M. Gender differences in cognitive impairment and mobility disability in old age: A cross-sectional study in Ouagadougou, Burkina Faso. Arch Gerontol Geriatr 2013;57:311-8.  Back to cited text no. 31
    
32.
Dessalegn M, Mulugeta T, Bhailu S. Cognitive impairment among type 2 diabetes mellitus patients in Ethiopia. Int J Med Appl Sci 2014;2:40-53.  Back to cited text no. 32
    
33.
Chen SD, Tang HD, Zhou Y, Fang R, Liu LH. Clinical characteristics for the relationship between type-2 diabetes mellitus and cognitive impairment: A cross-sectional study. Aging Dis 2015;6:236-44.  Back to cited text no. 33
    
34.
Kumari M, Eric B, Rebecca F. Minireview: Mechanisms by which the metabolic syndrome impair memory. J Gerontol 2000;53:228-32.  Back to cited text no. 34
    
35.
Mayeda ER, Whitmer RA, Yaffe K. Diabetes and cognition. Clin Geriatr Med 2015;57:311-8.  Back to cited text no. 35
    
36.
Koekkoek PS, Kappelle LJ, van den Berg E, Rutten GEHM, Biessels GJ. Cognitive function in patients with diabetes mellitus: Guidance for daily care. Lancet Neurol 2015;14:329-40.  Back to cited text no. 36
    
37.
Tangvarasittichai S. Oxidative stress, insulin resistance, dyslipidemia and type 2 diabetes mellitus. World J Diabetes 2015;6:456-80.  Back to cited text no. 37
    
38.
Lapolla A, Mosca A, Fedele A. The general use of glycated haemoglobin for the diagnosis of diabetes and other categories of glucose intolerance: Still a long way to go. Nutr Metab Cardiovasc Dis 2011;21:467-75.  Back to cited text no. 38
    
39.
Bello N, Dawakin-Kudu AT, Adetokun AB, Taura DW, Jobbi YD, Umar M et al. Characterization and antimicrobial suspectibility profile of bacteraemia causing pathogens isolated from febrile children with and without sickle cell disease in Kano, Nigeria. Mediterr J Haematol Infect Dis 2017;10:1-9.  Back to cited text no. 39
    
40.
Epstein M, James RS. Diabetes mellitus and hypertension. Hypertension 1992;19:403-18.  Back to cited text no. 40
    
41.
Campbell NR, Leiter LA, Larochelle P, Tobe S, Chockalingam A, Ward R et al. Hypertension in diabetes: A call to action. Can J Cardiol 2009;25:299-302.  Back to cited text no. 41
    



 
 
    Tables

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