Assignment: Article Critique Overview of the Study

Ismail, Materwala, & Al Kaabi’s (2021) study titled “Association of Risk Factors with
Type 2 Diabetes: A Systematic Review” was selected for critique. The two primary purposes of
this systematic review are to determine the leading risk factors for the prevalence/incidence of
diabetes mellitus type 2 (DMT2) and provide a critical evaluation of the cross-sectional/cohort
studies examining the effect of the correlation of risk factors on DMT2. Subsequently, the
research gives insights into risk variables whose interactions are key contributors to developing
diabetes. The study offers recommendations to allied government agencies, individuals, and
health professionals to support a better prognosis and diagnosis of diabetes mellitus type 2.
To answer their questions, Ismail, Materwala, & Al Kaabi (2021) conducted a systematic
literature search in nine clinical databases, including Web of Science, Springer, Scopus,
ScienceDirect, PubMed Central, MEDLINE, Embase, IEEE Xplore, and CINAHL. Their main
objective was to collect all relevant empirical evidence examining the correlation between the
prevalence/incidence of DMT2 and individual risk factors. The appropriate studies have to meet
six inclusion criteria: (1) diabetes type 2 as the specific risk, (2) use at least one risk factor, (3)
prospective cross-sectional or cohort study, (4) published in English, (5) and measure findings in
terms of Hazard Ratio (HR), Relative Risk/Risk Ratio (RR), or Odds Ratio (OR) with a 95% CI
for the correlation between type 2 diabetes and the risk factor. Out of 2,525,767 studies
originally retrieved, only 106 met the inclusion criteria and were thus considered for the review.
The results show that physical inactivity, family history of DMT2, ethnicity, hypertension,
dyslipidemia, smoking, and sleep quality/quantity are strongly interrelated with developing type
2 diabetes mellitus. However, the relationship between type 2 diabetes and serum uric acid is

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unclear. The researchers could not exclusively confirm serum uric acid as an independent
predictor or risk factor for diabetes mellitus type 2. However, serum uric acid only strengthens
the link between other independent risk factors like dyslipidemia, hypertension, obesity, and type
2 diabetes. Additionally, the study shows a possibility of a reverse relationship existing between
serum uric acid and type 2 diabetes, meaning that diabetes leads to an increase in serum uric acid
quantities in the blood.

Strengths of the Study

One real benefit of the study stems from its research design: a systematic review.
Typically, a systematic review summarizes and synthesizes the best available medical literature
on a specific topic, providing practical evidence for informed decision-making (Gopalakrishnan
& Ganeshkumar, 2013). The selected article systematically evaluates and synthesizes the best
available literature exploring key risk factors predisposing people to diabetes mellitus type 2.
Another advantage of using a systematic review is that the approach uses reproducible and
explicit methodologies to search systematically, critically analyze, and statistically synthesize a
specific health issue. Moreover, it synthesizes findings of different but interrelated primary
studies by using approaches that lower random errors and biases.
Therefore, by adhering to strict scientific approaches and designs based on reproducible,
specified, and explicit methods, the systematic reviews provide accurate and reliable estimates
and account for the risk factors predisposing people to type 2 diabetes mellitus. The study design
also effectively and accurately identifies gaps in research and proposes guidelines for future
research. For instance, the study found no strong evidence linking serum uric acid and diabetes
type 2, suggesting the need for future research to validate the relationship using more
standardized measurement techniques.

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The second advantage is that the study only evaluates, summarizes, and synthesizes
quality and non-biased articles across all the risk factors. Specifically, the study uses the NIH
(National Institutes of Health) quality assessment tool to examine each study’s quality, bias risk,
and validity. This screening tool consists of fourteen questions that analyze a study’s bias risk
and validity. Using words like not reported, not applicable, cannot be determined, yes, and no,
the researchers classified each study as poor (low quality), fair (moderate quality), and good
(high quality). This ensured that the articles selected were of the highest quality and less biased
(NIH, 2021).
The third advantage is that the study generally evaluated a substantial number of articles
for nearly all of the diabetes mellitus type 2 risk factors examined, including serum uric acid
(n=12), obesity (n=8), family history of diabetes (n=13), ethnicity (n=6), hypertension (n=6),
depression (n=8), smoking (n=24), and sleep quality/quantity (n=25). Only dyslipidemia (n=3)
and cardiovascular disease (n=1) had the lowest number of articles. Moreover, most articles
selected had a significantly large sample size. For example, two of the twelve articles selected
for serum uric acid had sample sizes of 7577 (Perry et al., 1995) and 481 (Chou et al., 1998).
Reviewing and synthesizing multiple articles with large sample sizes increases the validity and
reliability of the study results, making it plausible to extrapolate or apply them to the general
population.

Weaknesses of the Study

One weakness of the study stems from the limited sample sizes used, especially the total
number of articles, for some risk factors. Despite the researchers generally reviewing and
synthesizing more than six articles for eight of the ten risk factors, only three studies and one
study was included for dyslipidemia and cardiovascular disease, respectively. Using such a small

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sample size dents the validity and reliability of information about the correlation between
dyslipidemia and cardiovascular disease as risk factors and type 2 diabetes mellitus. This makes
it challenging to extrapolate or generalize the findings.
The second setback of the study emanates from the decision to zero down to only
prospective cohort and cross-sectional studies. Each of the two study designs presents unique
challenges. For example, prospective cohort studies are prone to bias because of follow-up loss.
The design is also vulnerable to confounding, and exposure status awareness might bias outcome
classification (Barria, 2018). The approach has a high propensity to produce biased outcomes
since most of the recorded data is observed and reported by the researcher or self-reported by the
participants. For example, Ismail, Materwala, & Al Kaabi (202) note in their conclusion that one
challenge with analyzing the correlation between sleep quality/quantity and type 2 diabetes is
that most data recorded is self-reported by study participants, which is prone to bias and
manipulation. This affects the overall validity and reliability of the outcomes.
At the same time, the disadvantages of relying entirely on cross-sectional studies are
multiple and far-reaching, especially for behavioral research. Cross-sectional studies cannot
determine cause and effect relationships, cannot be employed to examine behavior over a
specific period, the timing of the snapshot might not be representative of the population, and the
outcomes might be skewed or flawed. Cross-sectional studies are also prone to bias, particularly
when non-responses are recorded. It becomes problematic if the features/characteristics of non-
respondents differ from those who fail to respond within the prism of the generalized population.
Trying to draw conclusions from such data is nearly useless since bias erases an entire subgroup
from the study. Furthermore, information misclassification can result in bias.
Recommendations to Improve the Study’s Quality

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There are two possible ways to improve the study’s quality and the validity and reliability
of its outcomes. One such way is by ensuring that an adequate number of articles are
appropriately screened, analyzed, and reviewed for all the risk factors, including dyslipidemia
and cardiovascular disease. For the two risk elements, which undeniably had the lowest number
of articles and sample sizes, the researchers can add six additional properly screened (for quality
and bias) articles using the NIH quality assessment tool. This will automatically increase the
sample size for the two risk factors, ultimately enhancing the validity and reliability of their
outcomes. This will make it appropriate to draw conclusions and apply study findings and
recommendations to the general population.
The second way to improve the study’s quality is by incorporating experimental studies,
randomized controlled trials, and systematic research reviews. This will address the inherent
concerns and challenges posed by cross-sectional and prospective cohort studies, including the
inability to measure cause and effect and response bias (Rosenberger & Lachin, 2015).
Final Summary – Study’s Implications for the Nursing Practice
The outcomes of this study provide an overall image of the major causative and risk
factors associated with the onset of diabetes mellitus type 2, including physical inactivity, family
history of DMT2, ethnicity, hypertension, dyslipidemia, smoking, and sleep quality/quantity. It
also paints a picture of what nursing professionals and other allied healthcare experts,
individuals, and government agencies can do to lower the incidences of diabetes at the micro,
meso, and macro levels. For example, at the micro-level, individuals and families can lower
diabetes incidences and prevalence by adopting a healthy lifestyle, which entails consuming
more vegetables and fruits, polyunsaturated fatty acids; engaging in exercise; and avoiding
smoking. According to Peimani, Tabatabaei, & Paajouhi (2020), the nurse’s role ranges from

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educating patients and the public about diabetes risk factors and potential preventive measures to
treating patients using appropriate medications.

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References

Barria, R. M. (2018). Cohort studies in health sciences. BoD – Books on Demand.
Gopalakrishnan, S., & Ganeshkumar, P. (2013). Systematic reviews and meta-analyses:
Understanding the best evidence in primary healthcare. Journal of Family Medicine and
Primary Care, 2(1), 9-14. doi: 10.4103/2249-4863.109934
Ismail, L., Materwala, H., & Al Kaabi, J. (2021). Association of risk factors with type 2 diabetes:
A systematic review. Computational and Structural Biotechnology Journal, 19, 1759-
1785.
NIH. (2021). Study quality assessment tools. https://www.nhlbi.nih.gov/health-topics/study-
quality-assessment-tools
Peimani, M., Tabatabaei, O., & Paajouhi, M. (2020). Nurse’s role in diabetes care: A review.
Iranian Journal of Diabetes and Lipid Disorders, 9(4), 1-9.
https://www.researchgate.net/publication/236985435_Nurses’_Role_in_Diabetes_Care_A
_review
Rosenberger, W. F., & Lachin, J. M. (2015). Randomization in clinical trials: Theory and
practice. John Wiley & Sons.