Factors influencing the risk assessment for the development of type 2 diabetes mellitus: a narrative review

Introduction

Type 2 diabetes mellitus (T2DM) is a prevalent chronic condition in adults globally, showing increased rates over the past three decades. It involves insulin resistance, high blood sugar, and obesity, posing systemic risks like cardiovascular diseases and renal failure. T2DM is influenced by genetic and environmental factors (1). Approximately 422 million people worldwide suffer from T2DM, causing 1.5 million deaths, with rising cases in both lower and higher income countries (2). New Zealand reported 277,803 diabetes cases in 2021, notably higher among the Māori and Pacific communities (3).

T2DM contributes significantly to chronic disease burden, driven by genetic, environmental, and modifiable risks. Small lifestyle changes can aid in prevention. Effective risk assessment tools are crucial but influenced by various factors with serious implications (4). Non-communicable diseases, including T2DM, have surged recently. Early detection and risk assessment are pivotal in prevention. Major risk factors include genetics, obesity, sedentary lifestyle, diet, smoking, gestational diabetes, micronutrient deficiencies, and stress (5,6).

This review aims to explore primary research on risk factors affecting T2DM development risk assessment. Modifiable factors impacting blood sugar were identified across multiple studies, fulfilling specified inclusion criteria to better understand their influence.

The diabetes assessment tool facilitates the identification of individuals at heightened risk of developing T2DM. This aids in early detection, management, and prevention of the disease. Such tools are categorized into invasive and non-invasive types (7,8). Non-invasive methods include screening questionnaires, while invasive methods encompass glycosylated hemoglobin (HbA1c), fasting blood sugar (FBS), postprandial blood sugar (PPBS), random blood sugar (RBS), and oral glucose tolerance test (OGTT). Various factors, such as medication (e.g., statins), hormones, body mass index (BMI), underlying medical conditions, stress, diet, altitude, family history, and age, have been identified as influencing risk assessment (9). HbA1c stands out as a pivotal tool in both the diagnosis and ongoing monitoring of diabetes mellitus. However, alongside its widespread adoption, it has become evident that non-glycemic variables can also impact HbA1c levels, potentially leading to inconsistent clinical implications (10).

Impact of ethnicity on T2DM risk assessment

Ethnicity due to the genetic variation has an impact on the T2DM assessment. A cross-sectional study (11) in different ethnic groups in UK analysed that Asia and black ethnic group had a higher prevalence of T2DM in comparison to white and other ethnic groups. If we analyse the findings of another study (12), it was found that several underlying pathophysiological and physiological causes are responsible for the higher HbA1c in different ethnicity. It was concluded that Asian and black non-Hispanic American ethnic group had higher HbA1c in comparison to the white non-Hispanic American ethnic group.

Impact of BMI on T2DM risk assessment

Increased body weight considered to be one of the compelling causes of the insulin insensitivity in the body. Research conducted in several African and south Asian countries have concluded a strong correlation between FBS and BMI. Another study (13) argued that there are several hormonal changes take place in the population with higher BMI that brings the imbalance between the gluconeogenesis and insulin sensitivity leading to the hyperglycaemia. This has direct impact on the FBS in long term. BMI is highly attributable risk factor of T2DM. A study done in Andean Latin American population (14) analysed that high BMI and obesity were the main factors influencing the risk assessment of T2DM specially in urban and high-income communities.

Impact of underlying diseases on T2DM risk assessment

Thyroid hormone is one of the most important determining factors of glucose homeostasis. People suffering from hypothyroidism are more prevalent to insulin resistance. Since thyroid hormone is a well-known contributing factor for metabolic syndromes a research study on Korean population concluded that free thyroxin and FBS has a positive correlation with each other. Individuals with the thyroid dysfunction are found to have high FBS levels in comparison to the other individuals. Known cases of hypertension, kidney diseases and pulmonary hypertension are some of the main pathological conditions which lead to the insulin resistance in the body and have direct impact on the blood sugar levels of the body leading to the difference in the readings of the diabetic assessment tools (15).

Impact of medicines on T2DM risk assessment

Drug induced hyperglycaemia is the result of reduced insulin secretion and increased insulin resistance. Impact of the drugs such as statins, diuretics, beta blockers and antipsychotics are seen on the distal glucose regulatory pathways of hyperglycaemia leading to insulin resistance in the body thus having an impact on T2DM assessment (16).

Impact of stress on T2DM risk assessment

A study was done in China on the doctors for assessing the impact of occupational stress on blood glucose and blood lipids. This study analysed that blood glucose and lipid levels were found to be higher in the high-stress group in comparison to the low-stress group. This study additionally analysed that HbA1c levels were found to be higher among the population over 50 years in high-stress group (17).

Stress is the main reason for the cortisol overproduction which has a negative impact on insulin effect. The cortisol works against the insulin function by creating a barrier between the glucose absorption by the cells. This process leads to the increased blood sugar levels in blood thus, influencing the blood glucose values in T2DM assessment. Cortisol is found to be the inhibitors of insulin activity in the body (18).

Impact of family history and socioeconomic position on T2DM risk assessment

A cross-sectional study performed to evaluate the impact of low socioeconomic status and family history concluded that a positive family history of T2DM and social and economic position of the individual affects the risk of T2DM. Though the risk is higher in females in comparison to males; however, the risk remains threefold higher in both genders. Health-related behaviour and clinical factors are also equally responsible for the disease risk assessment in the population with low socioeconomic status and positive family history of T2DM (19).

Impact of age on T2DM risk assessment

T2DM is found to be impacting the aged population and also children. A study found that the values of HbA1c are higher in the obese children as well as the population above 40 years of age. A correlation was found between age and HbA1 diagnostic testing. The author concluded that the efficacy of HbA1c reduces with age of the person and cannot be found suitable for the T2DM diagnostic tool (10).

Impact of lifestyle and diet on T2DM risk assessment

Some studies have analysed the link between the consumption of soft aerated drinks and increased weight and T2DM. This may be due to the utilisation of fructose corn syrup in the soft drinks manufacturing. Upon consuming the soft drinks blood sugar levels are found to be increased malignantly. It is also concluded that diet drinks have Assy39 which is found to be the antagonist of the insulin activity in the body. Higher intake of fried food, junk food, red meat, sweets, white rice, and white flour also lead to the insulin resistance in the body (20).

Physical activity also plays an important part in individual’s life to maintain the metabolic activities. Contraction of the skeletal muscle improves the glucose uptake of the cells in the body. This helps in improving the glucose transportation in the muscles. This helps in increasing the insulin sensitivity in the body. Excess fat utilisation and reduction of the abdominal fat due to the physical activity also reduces the insulin sensitivity. Sedentary lifestyle with poor diet choices has an impact on the T2DM assessment (20,21). We present this article in accordance with the Narrative Review reporting checklist (available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-62/rc).

Methods

A narrative literature review was chosen as the research methodology. This approach involves an evidence-based analysis of published research articles relevant to the study’s objectives. The primary aim of a narrative review is to synthesize current knowledge in the field and highlight directions for future research (22,23). It provides a comprehensive analysis to gain a broader perspective on the research problem and identifies gaps in the existing literature (24). A wide-ranging review of literature was conducted to ensure a thorough and meaningful analysis and discussion. Articles were selected following a systematic screening process detailed in a flow diagram depicting identification, screening, and final inclusion of primary research articles (25,26). This literature review includes primary research selected according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, with quality assessed using the Critical Appraisal Skills Programme (CASP) tool (27). PRISMA guidelines were followed to identify and include screened articles based on predefined criteria, and CASP was employed to evaluate their quality.

A comprehensive literature search was conducted across several databases including ProQuest, PubMed, Google Scholar, and Scopus. Keywords such as T2DM, Diabetes Mellitus assessment tool, risk factors, ethnicity, family history, age, population, stress, HbA1c, FBS, PPBS, RBS, OGTT, underlying diseases, medication, physical activity, sedentary lifestyle, diet, food habits, BMI, and obesity were chosen in accordance with the study’s objectives and research question. Advanced search techniques utilizing Boolean operators (“AND”, “OR”, “NOT”) were employed to refine the search results. Various limiters such as timeline, peer-reviewed status, source type, language, and subject were applied to further tailor the search outcomes. The search strategy adhered strictly to predefined inclusion and exclusion criteria, as outlined in Table 1. Search into the reference list of the articles was also done to ensure the required articles not being missed. The given Table 2 contains the inclusion and exclusion criteria used for the primary articles search.

Primary research articles were retrieved from databases using specified keywords aligned with the research objectives. Articles were selected following PRISMA guidelines for inclusion and exclusion criteria. A PRISMA flowchart guides and illustrates the process of identifying, screening, and ultimately including primary research articles (25,26). A total of 669 articles were initially identified from ProQuest (n=285), PubMed (n=134), Google Scholar (n=50), and Scopus (n=200). These were refined by excluding reviews, meta-analyses, secondary research, and grey literature. Subsequently, 42 studies were reviewed, with 32 articles excluded based on abstract and inclusion/exclusion criteria, resulting in 10 studies being shortlisted. A manual search was also conducted to ensure comprehensive coverage, confirming data saturation with recurring articles by similar authors. Figure 1 depicts the detailed process of identification, screening, and inclusion.

Figure 1 PRISMA flowchart (25). PRISMA, Preferred Reporting Items for Systematic reviews and Meta-Analyses.

The evaluation of the quality of the articles was done using CASP tool for the qualitative studies. Each of the 10 articles were evaluated as per the CASP checklist questions for the qualitative studies. The questions and results are being represented in the following Tables 3,4. An overview of summary of findings from the 10 selected articles is given in Table 5.

Data analysis was done with the help of thematic analysis. Thematic analysis helps to recognise the different themes and subthemes in relation to the aim of the study which helps to analyse the data and answer the research question (34). Since the focus of the study was to analyse the factors influencing the risk assessment of T2DM, this review is the analysis of primary studies related to the topic of scholarly project.

Ten primary studies published in peer reviewed journals meeting the inclusion and exclusion criteria were selected and analysed. On evaluation of these articles, themes and subthemes were generated. All the studies were related to the aim and objective of the research topic. Themes and subthemes are listed in the Table 6.

This literature review was not a primary or secondary research, this did not involve the subject participation and critical information for data collection. In many countries like UK, Australia, Netherlands and United States of America (USA), nonidentifiable data is exempted and does not require formal approval (35). Reviews are the work from the evaluation and analysis of primary research evidence it is necessary to keep the cultural and ethical aspect in mind. Even though this literature review is low-risk research, but an ethical approval was acquired from Research Ethical Approval Committee of Eastern Institution of Technology, under the reference number SONHSS21/50.

Key findings

The key findings from the 10 selected primary studies reviewed in this literature analysis was examined and interpreted through thematic and sub-thematic categories aligned with the study’s aims and objectives. The findings of these selected articles were subjected to a rigorous analysis to facilitate a comprehensive discussion of the findings as mandated by the study’s objectives. From these 10 articles, three primary themes were identified, each further subdivided into nine sub-themes as given in Table 6.

Theme 1: biological factors

Ethnicity

Individual genetic structure is determined by several factors including race and ethnicity. A population-based study done in UK primary care setting (11) found that T2DM was more prevalent in Asian and Black ethnic groups in comparison to white ethnic group. The study analysed that body fat index was the major reason behind ethnic differences among T2DM patients. Higher fat mass was more prevalent among the Asian and black ethnic group in comparison to white ethnic group (11,14).

BMI

BMI is one of the major influencing factors because increased BMI is associated with obesity which is one of the main causes for the insulin resistance. A population-based cohort study (14) among the Andean Latin-American found that high BMI attributed to T2DM 45% in men and 57.2% in women. T2DM is caused due to the physiological changes in body over a period and leads to the increased blood sugar in individuals. High BMI is found to have impact on the glycaemic index of the individual thus influences the risk assessment (11,14).

Family history

A positive family history of T2DM significantly increases the risk of developing the condition. Baig et al. [2020] conducted a case-control study demonstrating that individuals with a family history of T2DM exhibit heightened oxidative stress and inflammation, which negatively impact insulin sensitivity, particularly in the postprandial state (30).

van Zon et al. [2017] found that lower socioeconomic status, combined with a family history of T2DM, correlates with DNA methylation patterns affecting inflammation and influencing insulin sensitivity, thereby contributing to elevated glycemic levels (19).

Johari et al. [2021], using data from the Kharameh cohort study within the Prospective Epidemiological Research Studies in Iran, conducted a cross-sectional analysis confirming that a positive family history of T2DM increases the likelihood of developing diabetes due to genetic factors (28).

In another study, Chetty and Pillay [2021] investigated the association between family history of T2DM and outcomes among human immunodeficiency virus (HIV)-positive patients. They concluded that HIV-positive individuals with a maternal history of diabetes have higher HbA1c levels compared to those without a family history (29).

Age

Age is one of the influencing factors on the biochemical results of the blood work. Physiological functions of the body are impacted due to increased body fat, high insulin resistance, decreasing pancreatic beta cells due to aging. Risk of raised HbA1c levels was found to be higher in the people above the age of 50 years in comparison to the people under the age of 35 years (11,17,28).

Glycaemic control is impacted by the age blood sugar level are found to be relatively higher in the individuals above the age of 55 years in comparison to the individual below 35 years of age (29).

Theme 2: lifestyle factors

Diet

Diet significantly influences individuals’ lives, especially in maintaining metabolic health. A balanced, nutritious diet is crucial for growth and metabolic maintenance. T2DM, being a metabolic disorder, is partly influenced by diet. Extensive literature explores the relationship between diet and T2DM. Wang et al. [2018] conducted a notable study on adolescents in China, revealing an increasing prevalence of T2DM in younger populations. Their national cross-sectional survey included 16,434 participants aged 6–17 years, showing higher fasting blood glucose levels among boys and urban dwellers compared to girls and rural residents. Their findings also linked this trend to consumption of fried foods and obesity (32).

Physical activity

Physical activity status and relationship between the biochemical blood parameter is well known. Sedentary lifestyle is considered to be prominent cause of several non-communicable diseases such as T2DM. Among the studies selected for the purpose of this review, one study (28) emphasized on the relationship between physical activity status and blood sugar levels. Normal blood glucose levels are maintained by liver due to the process of glycogenolysis and gluconeogenesis. Physical activity is very important to control and prevent the insulin resistance in the body. Physical activity helps in maintaining the insulin sensitivity. This is an important factor and influences the risk assessment because it impacts the values of FBS and PPBS in the body.

Stress

Occupational stress has turned out to become an epidemic. It impacts the blood sugar levels in the body. A lot of biochemical changes could be seen in individuals with the stressful works profiles. Wang et al. [2019] conducted a trial to assess the impact of occupational stress on the doctors in Harbin Medical University in China (17). He analysed that HbA1c levels were found to higher in the high-stress group like surgeons and emergency doctors had higher blood glucose level in comparison to the other doctors working in otolaryngology, ophthalmology, and dermatology departments.

Theme 3: medical history

Underlying diseases

Anderson et al. [2019] conducted a cross-sectional survey in five villages of Tehri Garhwal (Uttarakhand), India, observing variations in HbA1c, FBS, and PPBS levels among individuals with iron deficiency anaemia and haemolytic anaemia (31). They concluded that the population in the Sub-Himalayan region with anaemia exhibited poor glycaemic control, explaining the diverse results in HbA1c, FBS, and PPBS. Chetty and Pillay et al. [2021] similarly found elevated HbA1c, FBS, and PPBS levels among HIV-positive patients, also noting higher triglyceride levels and hypertension as contributing factors (29).

Ongoing medication regime

Crandall et al. [2017] conducted a randomized clinical trial within the Diabetes Prevention Program to assess interventions aimed at delaying the onset of T2DM among high-risk individuals. Spanning 32 clinical centres across the USA, the study observed that participants using statins exhibited higher fasting glucose and HbA1c levels compared to other groups. Statin users also faced an increased risk of diabetes attributed to a lower insulinogenic index and reduced insulin secretion, impacting overall risk assessment within the cohort (33). Johari et al. [2021] further investigated the influence of medication history on biochemical parameters, noting significant impacts on blood sample results and a moderate decline in glycaemic control as measured by HbA1c (28).

Discussion

The primary objective of this narrative review was to explore the determinants influencing risk assessment for the development of T2DM. Various factors were reviewed in this context. The review categorized these factors into three principal themes: biological factors, subdivided into age, ethnicity, family history, and BMI; lifestyle factors, comprising diet, physical activity status, and stress; and medical history factors, encompassing underlying diseases and current medication regimens.

Biological factors and their influence on risk assessment for the development of T2DM

Biological factors such as ethnicity, genetic history, age, and BMI significantly influence the risk assessment for developing T2DM. Six out of 10 reviewed articles discussed these factors (11,14,19,28-30). Ethnicity plays a crucial role in T2DM risk, with Asians and Blacks exhibiting higher prevalence compared to Whites and other ethnic groups (11). Genetic history, particularly maternal diabetes, strongly impacts glycaemic control and T2DM risk (29). Age inversely correlates with HbA1c levels, with higher values observed in younger populations (18–30 years) compared to older ones (81–90 years) (29). Advancing age is a confounding factor due to decreased insulin sensitivity and higher blood glucose levels in older adults (55–70 years) (11,17,28). Maternal family history of T2DM increases susceptibility, evident even among HIV-positive individuals, suggesting a potential link between viral impact and elevated HbA1c levels (29).

Lifestyle factors and their impact on risk assessment for the development of T2DM

The selected articles underscore lifestyle as a significant factor in global T2DM prevalence. Key factors include diet, physical activity, and stress. Poor dietary choices, sedentary behaviour, smoking, alcohol, and oxidative stress decrease insulin sensitivity, disrupting metabolism and liver processes like gluconeogenesis and glycogenolysis. This disturbance leads to dysregulated glycaemic control, reflected in elevated FBS, PPBS, and HbA1c levels. Increased intake of sweets, sugary beverages, fatty and refined foods heighten T2DM risk (11,14,17,28,29,31,32).

Occupational stress and negative emotions elevate HbA1c levels, suggesting a direct link to metabolic disorders such as T2DM (17). Studies highlight a robust association between diet, physical activity, and T2DM risk, noting significant biochemical changes. Poor glycaemic control is prevalent among those consuming excess sugar, processed foods, alcohol, and fried items. Adequate physical activity is crucial for metabolic health, yet modern sedentary lifestyles contribute to metabolic imbalance and poor glycaemic control (36,37).

Medical history and its impact on risk assessment for the development of T2DM

Among 10 articles reviewed, 3 addressed how underlying diseases and current medications affect glycaemic control. Conditions like iron deficiency anaemia, haemolytic anaemia, hypertension, and hyperlipidaemia correlate with elevated HbA1c and FBS. HIV patients also exhibit higher HbA1c levels (28,29,31).

One article highlighted the association between statin use and diabetes onset in a clinical trial assessing T2DM incidence among high-risk individuals. Statin users showed a 30% increase in blood sugar levels compared to non-users, with a corresponding 30% higher risk of diabetes. Longer duration of statin use was significantly linked to increased diabetes risk (33).

Drug-induced hyperglycaemia involves decreased insulin secretion and increased insulin resistance. Medications like statins, diuretics, beta blockers, and antipsychotics affect distal glucose regulatory pathways, thereby contributing to insulin resistance and impacting T2DM risk assessment (16).

Limitation and strength

The strength of the review is that the analysis of various themes and subthemes resulted in abundant data which provided varied findings for the future studies. The major limitation of the study was due to the narrative literature review framework. Since this kind of review does not have a particular framework. Another limitation was the selection of the articles from the peer reviewed journal articles, due to which the literature search was not completely exhausted, and many important studies must have been left out. This kind of review is mostly objective in nature as per the requirement of the study.

Conclusions

In conclusion, this study successfully achieved its objectives by thoroughly reviewing factors influencing T2DM risk assessment. The review commenced with an introduction outlining its aims, objectives, and methodology for selecting and analysing primary research articles. T2DM poses a significant global health concern recognized by the World Health Organization, with its prevalence steadily rising. Factors such as ethnicity, BMI, age, family history, diet, physical activity, lifestyle, stress, underlying diseases, and medication were analysed for their impact on T2DM risk assessment. These factors significantly influence glycaemic control and can contribute to the development of T2DM if left unmanaged. The findings of this review have implications for public health, suggesting the implementation of protocols and screening programs to prevent and manage the disease burden. Modifiable risk factors underscore the importance of primary healthcare prevention programs aimed at reducing disease incidence. Recommendations include conducting large-scale population-based research, implementing regular blood sugar monitoring in primary healthcare settings, promoting health equity and literacy, tailoring healthcare measures to genetic and ethnic diversity, and developing policies for managing high-risk populations.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-62/rc

Peer Review File: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-62/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-62/coif). S.M.S. serves as an unpaid editorial board member of Journal of Public Health and Emergency from December 2022 to November 2024. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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