Annals of Alzheimer's and Dementia Care
Research Article       Open Access      Peer-Reviewed

Roles of demographics, anthropometric and metabolic syndrome on cognition among mid adults from rural population in Nigeria

Valentine A Ucheagwu1*, Cyriacus Ajaelu2, Paul C Okoli3, Jesse Ossai2, Philip C Ofojebe2 and Rita Ugokwe-Joseph2

1Department of Psychology, Chukwuemeka Odumegwu Ojukwu University, Igbariam Campus, Laboratory of Human and Animal Psychology, Madonna University Okija Campus, Nigeria
2Department of Psychology, Nnamdi Azikiwe University, Awka, Nigeria
3Department of Internal Medicine, Enugu State University of Technology, College of Medicine, Nigeria
*Corresponding author: Valentine A Ucheagwu, Department of Psychology, Chukwuemeka Odumegwu Ojukwu University, Igbariam Campus, Laboratory of Human and Animal Psychology, Madonna University Okija Campus, Nigeria, E-mail:
Received:28 June, 2019 | Accepted: 12 August, 2019 | Published: 13 August, 2019
Keywords: Processing speed; Cognition; Metabolic syndrome; BMI

Cite this as

Ucheagwu VA, Ajaelu C, Okoli PC, Ossai J, Ofojebe PC, et al. (2019) Roles of demographics, anthropometric and metabolic syndrome on cognition among mid adults from rural population in Nigeria. Ann Alzheimers Dement Care 3(1): 003-010. DOI: 10.17352/aadc.000007

There is increased interest in cognitive aging among experts in brain sciences and gerontology as a result of improvement in health sciences leading to longeveity. However, there are very limited studies on this area in Nigeria and some parts of Africa. Our study was to contribute to this area of science with African population, hence we investigated the roles of demographics, anthropometry and metabolic syndrome (Mets) on cognition in mid adults from rural part of Nigeria. Our participants were 172 middle aged adults from a rural community in Anambra state Nigeria. Their age range were between 50 – 70 years with a mean age of 57.48 and SD age of 5.48. The Montreal Cognitive Assessment (MoCA) Symbol Digit Modality Test (SDMT) and Digit Symbol Test (DST) were used to assess the participants’ cognition. The anthropometric measures assessed were the body mass index (BMI), height, weight and waist circumference while the Mets assessed were blood sugar level and total cholesterol and blood pressure. Gender, occupation and education were included as demographic factors assessed in the study. The results show the BMI, weight and waist circumference as predictors of general cognition, but the blood pressure and height were not significant predictors. Equally, mixed findings were got on processing speed. The variables measured were not significant predictors of SDMT as a measure of processing speed while they to significant extent predicted processing speed when DST was used.


Cognitive health among mid and late adult population is very important for their overall wellbeing and adjustment. Interest on how to promote healthy cognition as people age has been major concern to health care. Metabolic syndrome (mets) is a cluster of biochemical and physiological conditions associated with the development of cardiovascular disease [1], and type 2 diabetes. Among the biochemical conditions may include abdominal obesity, high blood pressure, high serum triglyceride and low high density lipoprotein (HDL) levels.

In a selected review, Yates, Sweat and Convit [2], describe the literature on the impact of mets on brain and cognition in adults and adolescents. In their review, most studies found associations between mets and cognitive dysfunction with adults having multiple cognitive domains affected while adolescents were mostly in executive functioning. Yates’ et al., [2], review implicated mets in ischemic stroke, white matter alterations and altered brain metabolism. For adolescents, mets factors were linked to volume losses in the hippocampus and frontal lobes. Overall, Yates and Colleagues review show that mets negatively impacts cognitive performance and brain structure. Its potential explanatory models include impaired vascular reactivity and abnormal brain lipid metabolism [2]. Yaffe, Kanaya, Lindquist, Simonsick, Harris, Shorr, Tylavsky and Newman [3], equally showed that elders with metabolic syndrome were more likely to have cognitive impairment compared with those without metabolic syndrome. They also showed that significant interaction of inflammation and metabolic system on cognition existed in the elderlies. After stratifying for inflammation, those with metabolic syndrome and high inflammation had an increased likelihood of cognitive impairment when compared to those without metabolic syndrome, while those with metabolic syndrome and low inflammation did not exhibit increased likelihood of impairment [3]. Other studies like Yaffe, Weston, Blackwill and Krueger [4], Gatto, Henderson, St John, c, Hodis and Mack Komulainen, Lakka, Kivipelto, Hassinen, Helkala, Haapala et al., [5], corroborated with the reports of Yaffe and colleagues [3].

Gottesman, Schneider, Albert, Alonso, Bandeen-Roche, Coker, Coresh and Co-workers [6], investigated midlife hypertension and 20-year cognitive change among African Americans and Caucasians. The cognitive change outcome included the 20-year change in scores on the Delayed Word Recall Test, Digit Symbol Substitution Test and Word Fluency Test as well as global cognition. Their findings show that baseline hypertension was associated with additional decline of 0.06 global cognition during 20 years and pre hypertension was associated non significantly with 0.04 global decline. Overall, they showed that midlife hypertension and elevated midlife but not late life systolic BP was associated with more cognitive decline during the 20 years of the study. Equally greater decline was found with higher midlife BP in white than in African Americans. There are extensive evidence emanating from studies of varying methodologies indicating that hypertension is associated with poorer cognitive performance on neuropsychological assessments including attention, learning and memory, executive functions and that clinical hypertension also predicts cognitive decline over time [7].

Investigations assessing the relationship between cholesterol level and cognitive functioning in healthy samples indicate that certain abilities may be inversely associated with serum cholesterol whereas others appear to be positively correlated with cholesterol concentration [8,9], evaluated associations between high density lipoprotein cholesterol (HDL) and non-HDL-C levels at specific ages and subsequent alzheimer disease (AD) risk. For non HDL-C, they found a significant association with AD risk in the 60 to 69 and 70 – 79 age bands, suggesting a potential U-shaped relationship (greater risk at low and high level). For example, in people aged 60 to 69, those with an average non HDL-C level of 120mg/DL had a 29% greater AD hazard than those with an average non HDL-C level of 160mg/DL, whereas those with an average non HDL-C level of 210mg/dl had a 16% greater hazard. However, Marcum and Colleagues (2018) did not find statistically significant association between HDL-C and AD risk. Similarly, Nooyens, Gelder, de-Mesquita, Boxtel and Verschuren [10], found that higher cholesterol intake was associated with faster cognitive decline, while higher n-z PUFA (especially a-lindenic acid) intakes was associated with slower decline in global cognitive function and memory as intakes of other fatty acids were not associated with cognitive decline. Sun, Lee, Ma and Kwok [11], had equally shown that among older people with diabetes mellitus, higher serum HDL-C was associated with better executive function. Also, Power and Colleagues (2018) showed that elevated total cholesterol, low density lipoprotein cholesterol and triglyceride were associated with greater 20-year decline in a test of executive function, sustained attention and processing speed.

On the other hand, blood sugar level has been associated with cognitive impairments in adult population. Altschul, Starr and Deary [12], assessed the correlation of cognitive function in early and later life and blood glucose in 1091 participants from Lothian Birth Control of 1936. Overall their findings showed high blood glucose to be consistently predicted by lower cognitive functions. Chaytor, Barbosa-Leiker, Ryan, Germine, Hirsch and Weinstock [13], showed that clinically significant cognitive impairment occurred in 48% of older adults with type 1 diabetes. After controlling for age, gender, education and diabetes duration, they showed that hypoglycemia unawareness, recent severe hypoglycemic events, microvascular complication, higher HbAIC and continuous glucose monitoring (CGM) average nocturnal glucose were all associated with increased odds of clinically significant cognitive impairment. However, CGM nocturnal % time below 60mg/dl was associated with a decreased odd of cognitive impairment. According to the authors, diabetes duration, age at diagnosis, daytime CGM and lifetime severe hypoglycemic events were not related to cognitive impairment status. Elfassy, Ajello, Schneiderman, Haan, Tarraf, Gonzalez, Gellman and colleagues [14], showed subgroup differences on relation of diabetes to cognitive function in Hispanics/Latinos of diverse backgrounds in the United States. Effassy and colleagues’ (2018) findings showed that compared with having normal glucose regulation, having diabetes was associated with worse processing speed among Cubans and Mexicans. As well compared with having normal glucose regulation, having pre-diabetes or diabetes was associated with worse delayed recall only among Mexicans. In essence, relationship between diabetes and cognitive function varied across Hispanic/Latino subgroups. Potentially, recent findings show strong relationships between blood glucose and cognitive decline in adult population. Kong, Park, Lee, Cho and Moon [15], showed increased insulin resistance to be significantly correlated with decreased cognitive function during a 6 year follow up study of older Koreans with normal baseline cognitive function.

Buch, Carmeli, Shefer, Keinan-Boker, Berner, Marcus, Goldsmith and Stern, [16], studied whether specific obesity phenotypes in community dwelling elderly affect differently the relationship between frailty and functional impairment and are equally related to cognitive impairment. Among other result outcomes, they showed that rate of cognitive impairment was 3.3 times higher in women who were obese by waist circumference but not by Body Mass Index (BMI). Mais, Georgiopoulos, Khan, Johnson, Wong, Charakida, Whincup et al., [17], studied patterns of adiposity, vascular phenotypes and cognitive function in the 1946 British Birth Cohort. Overall, their findings showed longer exposure to elevated waist circumference (WC) or BMI and faster WC or BMI gains between 36 and 43 years are related to lower cognitive function at 60-64 years. Equally, patterns of WC in adulthood could provide additional information in predicting late midlife cognitive function than patterns of BMI. Mangone, Yates, Sweat, Joseph and Convict [18], showed that executive function (EF) is negatively implicated in adolescents with mets. Additionally, waist circumference was determined to be significant predictor of the executive function (EF) deficits. Other studies like Bugge, Meller, Westfall, Tarp, Geji, Wedderkopp and Hillman [19], also supported the relationship between WC and other mets on cognitive function in adolescents.

Overall, findings from literature points to the contributions of mets and individual metabolic factors on cognitive performance in both adult and adolescent population. However little or no studies to the best of our knowledge have been done in Nigeria as regards to this. It is true that African American population have been studied to some extent, however, their findings cannot be readily extrapolated to African population. This problem of paucity of research of African origin limits global picture on causation/etiology of dementia. Equally general studies on mets have always considered global cognitive measures. Little effort has gone into evaluating the role of mets and anthropometry on processing speed. The present study therefore seeks to answer the following research question: (1) Do metabolic syndrome and its independent factors (BP, sugar level, total cholesterol, BMI) predict significantly cognitive performance (general cognition and processing speed) in mid adults from rural population in South-East Nigeria? (2) What are the contributions of some demographic variables (education, occupation, gender, age) and anthropometric measure (Height, weight) on cognitive performance in the same population? We hypothesise that metabolic syndrome, education and gender will significantly predict global cognitive performance and processing speed in the sample studied.



The sample included 172 middle age adults from a rural community in Anambra State Nigeria. Their age range was between 50 – 70 years with a mean age of 57.48 and SD age of 5.45. Table 1 shows the descriptive statistics of the participants. For the present study, the young mid age were participants between 50-59 age while the old mid age were those between the ages of 60-70 years. The professional category are the participants with minimum of tertiary education who were engaged in professional jobs like teachers, counsellors, physicians. The non professional category for the present study were participants engaged in other forms of jobs like trading, hand craft and low level secretary and assistants. They were mostly with primary and secondary education.


The Montreal Cognitive Assessment (MoCA) was used to assess general cognition among participants. MoCA developed by Nasreddine in 1996 [20], is a widely used screening assessment for detecting cognitive impairment. It has been validated in clinical settings assessing mild cognitive impairment and has subsequently been adopted in numerous other clinical settings. The Symbol Digit Modalities Test (SDMT) [21,22], was developed to identify individuals with neurological impairment. The SDMT assesses key neurocognitive functions that underlie many substitution tasks including attention, visual scanning and motor speed. The SDMT is an altered inverse form of Digit Symbol Test (DST) [23], equally used in the present study. Correlations of the SDMT with the Digit Symbol Test, test-retest correlations for the SDMT as well as the correlation between its written and oral administrations are all on the order of 0.80 for normal subjects [24,25]. For the study, the SDMT and DST were used to assess the participant’ processing speed.

The Body Mass Index (BMI) was calculated as the ratio of weight (in kilograms) to height (in meters) squared. Height and weight were measured objectively with a clinical calibrate scale. Waist circumference were obtained with a flexible tape measure, manipulated to maintain close contact with the skin without compression of underlying tissues. Waist circumference was defined as the minimal abdominal perimeter located half way between the rib cage and pelvic crest. The metabolic syndrome were got through the use automated diagnostic kits for blood sugar level and cholesterol level measurement. Equally, automated sphygmomanometer kit for blood pressure assessment was used to measure the blood pressure of the participants.


The participants for the study were recruited through weekend door to door knocking in the community. This way, the research assistants were able to identify the participants within the given age bracket of the study interest. The houses were mapped for visit. The testing were done in the participants’ houses where they were more comfortable to respond. All the questionnaires and the neuropsychology tests were administered the same day for each participant with some breaks at intervals. The blood samples for the cholesterol and sugar analysis were equally taken at the end of the exercise. The research assistants explained to the participants that the study was to determine the contributions of some variables on cognitive functioning and solicited for their co-operations. They were all required to sign informed consent for the study which all complied. For few that were unable to read and understand the consent form, their relatives explained to them and then had to consent after they had understood the form.

Design and Statistic

The study was a community survey design. Multiple regression analysis and a 3-Way Multiple Analysis of Covariance (MANCOVA) statistics were used in analyzing the data. The regression analysis was used to determine the predictive strengths of the predictor variables (metabolic syndrome and anthropometry) on cognition, while the MANCOVA was used to analyse the test of between differences of the demographic variables on cognition. A pairwise comparisons was used as a post hoc analysis for significant demographic variable.


Metabolic and Anthropometric Predictors of Cognition

Our analysis started with a stepwise regression of the cardiovascular markers of metabolic syndrome. The stepwise regression shows the systolic blood pressure (BP) as the only significant predictor of general cognition based on the MoCA, with standardized co-efficient β = 0.15; while the diastolic BP; β = 0.006 and Pulse: β = 0.001 all at P ≤ 0.05 level of testing. The overall model contribution (R2) was 0.02, while the ANOVA model was not significant at P < 0.05. The data show that the systolic blood pressure accounted for the overall contributions to the model. On the other hand, we entered other metabolic and anthropometric markers against MoCA and the findings show: BMI; β = 0.55, weight; β = 0.54 and waist circumference; β = 0.39 as significant predictors of general cognition. The sugar level; β = 0.20; total cholesterol; β = 0.18, and Height β = 0.003 were not significant predictors of general cognition.

The same variables were equally regressed for the digit symbol modality test (DSMT) as measure of processing speed. The Cardiovascular markers showed no significant predictions on the DSMT tasks and the ANOVA model fit was also not significant at P < 0.05. The overall contribution to the model (R2) was 0.04, while β scores for the individual variables were: systolic = 0.12; diastolic = 0.17 and pulse = 0.03. When the cardio markers were regressed on symbol digit test (SDT), the findings show an R2 of 0.11, while the ANOVA model fit was significant at P ≤ 0.002 level of testing. The standardized coefficients Symbol were systolic = 0.34; diastolic = 0.05 and pulse = 0.02. Only the systolic blood pressure had significant Symbol coefficient.

The other metabolic and anthropometric variables were also regressed in the model. The BMI, waist circumference, height and weight were first regressed on DSMT. Their overall model contribution was R2 = 0.05 and the ANOVA model fit showed no significance at P ≤ 0.05 level of testing. Equally the Symbol coefficients were not significant at P ≤ 0.05; BMI = 0.10, height = 0.12 and weight = 0.07. When regressed on SDT, the R2 was 0.14 while the ANOVA model fit was significant at P ≤ 0.002. The β coefficients had no significant predictions at P ≤ 0.05 except the waist circumference with Symbol = 0.20. The other Symbol coefficients were BMI = 0.17; Height = 0.18 and Weight = 0.03. The total cholesterol (TC) and sugar levels were also regressed on DSMT. As expected, their R2 was 0.53 though the ANOVA model was not significant at P ≤ 0.05. Their Symbolcoefficients were; sugar level = 0.52; total cholesterol = 0.31. However, the coefficients did not show significance at P ≤ 0.05 level of testing. On SDT, the total cholesterol and sugar level had the R2 of 0.66 while the ANOVA fit model was significant at P ≤ 0.02 level. The Symbol coefficients were sugar level = 0.65 (P ≤ 0.05) and cholesterol = 0.26 (P ≤ 0.34).

When age was regressed on MoCA, the finding shows little overall contribution of Age; R2 = 0.07, although the ANOVA model fit was significant at P ≤ 0.001. The Symbol coefficient was 0.27 at P ≤ 0.001. Education, occupation and gender were equally analysed as demographic variables using the 3 WAY MANCOVA with age a covariate. The findings show significant differences of education on the 3 measure of cognition studied: SDMT; F(2, 123) = 15.31; DSMT; F(2,123) = 8.95 and MoCA; F(2,123) = 5.32 all at P < 0.001 level. Their effect sizes were equally high ranging from 0.14 – 0.22. However, all other demographic variables showed no significant differences on cognition. The following were their MANCOVA measures; Occupation: SDMT, F(1, 123) = 0.59; DSMT, F (1, 123) = 0.98; MoCA, F(1, 123) = 0.04. Gender: SDMT, F(1, 123) = 0.01; DSMT, F (1, 123) = 1.27; MoCA, F(1, 123) = 0.42. Age as a covariate showed no significant difference. On the interaction effects, our findings show no interaction effect of the independent variables on cognition at P ≤ 0.05 except that of education* occupation on MoCA , F(2, 123) = 3.517, and education* gender on DSMT, F (2, 123) = 3.21, although with less effect sizes of 0.06 respectively. Table 2 shows the estimated marginal means of education on the dependent variables (cognition).

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© 2019 Ucheagwu VA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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