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Incidence and temporal trends in type 2 diabetes by weight status: A systematic review and meta-analysis of prospective cohort studies

Hong-jie Yu1, Mandy Ho1, Xiangxiang Liu2, Jundi Yang1, Pui Hing Chau1, Daniel Yee Tak Fong1

1 School of Nursing, University of Hong Kong, Hong Kong SAR, China
2 National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen, Shenzhen, China

DOI: 10.7189/jogh.13.04088




Diabetes is more prevalent among overweight/obese individuals, but has become a significant public health challenge among normal weight populations. In this meta-analysis, we aimed to estimate diabetes/prediabetes incidence and its temporal trends by weight status.


PubMed, Embase, Web of Science, and Cochrane Library were searched until 8 December 2021. Prospective cohort studies reporting diabetes incidence by baseline body mass index (BMI) categories in adults were included. The median year of data collection was used to assess the temporal trends. Subgroup analyses and meta-regression were also performed.


We included 94 studies involving 3.4 million adults from 22 countries. The pooled diabetes incidence in underweight, normal-weight, and overweight/obese adults was 4.5 (95% confidence interval (CI) = 2.8-7.3), 2.7 (95% CI = 2.2-3.3), and 10.5 (95% CI = 9.3-11.8) per 1000 person-years, respectively. The diabetes incidence in low- and middle-income countries (LMICs) was higher than in high-income countries among normal-weight (5.8 vs 2.0 per 1000 person-years) or overweight/obese (15.9 vs 8.9 per 1000 person-years) adults. European and American regions had a higher diabetes incidence than the non-Western areas, regardless of weight status. Underweight diabetes incidence decreased significantly from 1995-2000 to 2005-2010. Diabetes incidence in normal-weight populations has increased continuously since 1985 by an estimated 36% every five years. In overweight/obese adults, diabetes incidence increased between 1985-1990 and 1995-2000, stabilised between 2000 and 2010, and spiked suddenly after 2010.


Diabetes incidence and its temporal trends differed by weight status. The continuous upward trend of diabetes incidence among overweight/obese individuals requires urgent attention, particularly in LMICs. Furthermore, diabetes among normal-weight individuals is becoming a significant public health problem.


PROSPERO (CRD42020215957).

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Obesity is a known risk factor for diabetes [1]. However, studies have suggested that diabetes in non-overweight individuals is becoming a significant public health challenge worldwide, particularly in Asian countries [25]. Likewise, a substantial proportion of individuals with normal weight also develop diabetes [3,4]. Results of trend analyses using data from nationally representative surveys reported that diabetes prevalence remains stable among adults with normal weight and overweight, while it increased and then dropped before leveling out among individuals with obesity over the past two decades [68]. However, prevalence is a less reliable metric than incidence in assessing changes in population risk for diabetes because increasing prevalence might be attributed to several factors such as increasing incidence, improved survival, and/or lower mortality rates [911]. To date, only one systematic review and a multicounty trend analysis in high- and middle-income settings has described the trends in diabetes incidence [10]. However, it did not provide data to quantify diabetes incidence and its temporal trends by weight status, which are of great importance for public health policies and clinical practices.

Additionally, the prevalence of prediabetes is nearly three times higher than that of diabetes, and pre-diabetes plays a critical role in the pathophysiology of type 2 diabetes (hereinafter referred to as diabetes) [12,13]. Thus, we conducted a systematic review and meta-analysis to estimate the incidence of prediabetes and diabetes among adults with underweight, normal weight, and overweight/obesity and their temporal trends.


We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines) [14] in conducting this systematic review and meta-analysis, and we preregistered its protocol in the International prospective register of systematic reviews (PROSPERO, CRD42020215957). We used the population, interventions/exposures, comparators, outcomes, and study design (PICOS) framework to develop the search strategy. We defined the population as adults (18 years and above) without diabetes at baseline, the intervention/exposures as different weight statuses defined by body mass index (BMI), outcomes as the incidence of prediabetes or diabetes, and we limited the study design to prospective cohort studies. The “comparator” criteria was not applicable in our review. We adapted the search strategy from a previous meta-analysis [15]. We searched PubMed, Embase, Web of Science, and Cochrane Library from inception to 8 December 2021 using keywords such as “diabetes”, “prediabetes”, “body mass index or BMI”, “obese”, “non-obese”, “cohort study”, and “adult”. We filtered the search results to “English” and “Human” studies (Table S1 in the Online Supplementary Document). We also identified additional studies from the reference list of two previous related meta-analyses [16,17].

We included prospective cohort studies with a follow-up duration >12 months, participants ≥18 years and free of prediabetes or diabetes at baseline, and with data on the cases and follow-up duration, incidence rate (cases/person-years), or cumulative incidence (%) by baseline BMI categories. We also included eligible studies from the same cohort but with different follow-up durations. For eligible studies from the same cohort and with a same follow-up duration, we only retained the one with a larger sample size. We excluded studies that targeted children or individuals with preexisting diabetes and related cardiovascular diseases and nerve, kidney, and eye damage and studies that combined prediabetes and diabetes as the main outcome without stratified analysis or measured type 1 or gestational diabetes as the main outcome. We defined prediabetes/diabetes as per the International Diabetes Federation [18] and American Diabetes Association [19].

Three authors (YHJ, LXX, and YJD) screened the title, abstract, and full text independently. Two authors (YHJ and LXX) extracted and checked the study characteristics, participants’ characteristics, exposure and outcome assessment, and baseline BMI categories using a standardised data extraction form. Two authors (YHJ and YJD) assessed study quality using the Newcastle-Ottawa scale (NOS) (Table S2 in the Online Supplementary Document) [20]; studies with a NOS score ≥7 were deemed good quality. We resolved all uncertainties through group discussion.

Data analysis

The primary outcome was the incidence rate of prediabetes or diabetes by weight status, calculated from the incident cases and corresponding person-years of follow-up, or as the cumulative incidence (%) divided by the median/average follow-up duration, if available [21]. We defined weight status by the ethnic-specific BMI classification recommended by World Health Organization (WHO): BMI (kg/m2)<18.5 as underweight, 18.5-22.9 for Asians and 18.5-24.9 for non-Asians as normal weight, ≥23 for Asians and ≥25 for non-Asians as overweight/obesity, respectively [22], with a margin of ±1 kg/m2 [23]. We calculated pooled estimates using the DerSimonian-Laird method [24] and used the Cochran’s Q test and I2 to assess statistical between-study heterogeneity [25].

We conducted subgroup analyses of pre-specified factors to explore potential sources of heterogeneity, including sex (female, male, and total), age (≤45, 45-60, and ≥60 years), country income evaluated per World Bank definitions (low- and middle-income countries (LMICs), and high-income countries (HICs)) [26], WHO region (Western Pacific Region, Region of the Americas, European Region, South-East Asia Region, and Eastern Mediterranean Region) [27], median follow-up duration (≤8 and >8 years), median year of data collection (before 1985, five-year interval between 1985 and 2010, and after 2010), study setting (rural, urban, and mixed), weight assessment (self-reported and directly measured), and prediabetes/diabetes ascertainment (via blood test, medical records, self-reported, and multiple methods). We defined “multiple methods” by blood test plus other one or two ascertainment methods.

We used the median year of data collection reported by the study to estimate temporal trends [2830] in three steps: by plotting the pooled incidence of diabetes/prediabetes against the median year of data collection in five-year intervals, by conducting a univariate meta-regression analysis with the median year of data collection as a continuous covariate and obtaining a bubble plot, and by conducting a multivariate meta-regression analysis to examine the robustness of temporal trends after including pre-specified factors with P ≤ 0.20 in their respective univariate meta-regression analysis [31]. We used the R2 to quantify the variance proportion explained by each model. We computed the correlation matrix and variance inflation factor (VIF) to assess the potential multicollinearity between pre-specified factors; we omitted one of two factors with correlation coefficients >0.5 or VIF>3.0 in the multivariate meta-regression model [32].

We conducted sensitivity analyses via the leave-one-out method, which re-pooled the incidence after omitting every single study to detect its contribution to overall heterogeneity [33]. Moreover, we repeated the meta-regression by only including good quality studies (NOS≥7) to test for robustness of results. We assessed publication bias via visual inspection of the funnel plot of standard error and Egger regression test when ≥10 incidences were available [25]. We performed all analyses using R, version 4.0.3. (R Core Team, Auckland, New Zealand) and its “metafor” and “meta” packages. We considered a two-tailed P-value <0.05 statistically significant.

Patient and public involvement

This systematic review and meta-analysis did not involve any raw personal data, so a patient and public involvement statement is not applicable.


Study characteristics

We retrieved 26 920 studies, of which 94 [34127] met the inclusion criteria (Figure S1 in the Online Supplementary Document). Ninety-two studies provided information on diabetes incidence (Table S3 in the Online Supplementary Document) [71115,118127]. We excluded one study from the meta-analysis because its BMI was categorised by quintiles without a clear description of the range [115]. The included studies covered 22 countries/regions and were published between 1991 and 2021, involving 3.4 million adults at baseline and observing about 184 000 cases during a median follow-up of eight years. The NOS score ranged from four to nine, and 78.7% (70/94) of studies were good quality (NOS≥7) (Table S2 in the Online Supplementary Document), with quality increasing with publication year.

Diabetes incidence by weight status

Fourteen studies [36,41,45,46,56,61,74,75,91,92,94,100,124,125] reported 23 estimates of diabetes incidences among adults with underweight. During a median follow-up of eight years (interquartile range (IQR) = 5-10), 1576 cases were identified. The pooled diabetes incidence among adults with underweight was 4.5 (95% confidence interval (CI) 2.8-7.3; I2 = 99.7%) cases per 1000 person-years (Figure 1, panel A). Among 73 studies that reported 83 estimates of diabetes incidence in adults with normal weight [3436,3840,42,4448,5055,5965,6769,7179,8184,87,8991,94110,112,114], 31 304 cases were identified during a median follow-up of eight years (IQR = 5-11.2). The pooled diabetes incidence in adults with normal weight was 2.7 (95% CI = 2.2-3.3; I2 = 99.5%) cases per 1000 person-years (Figure 1, panel B). Diabetes incidence among individuals with overweight/obesity was available in 91 studies [34114] and the pooled value was 10.5 (95% CI = 9.3-11.8; I2 = 99.7%) per 1000 person-years (Figure 1, panel C). The leave-one-out sensitivity analyses did not indicate the predominance of any single study for overall heterogeneity (Table S4 in the Online Supplementary Document). After excluding studies with NOS<7, the pooled incidences of diabetes in adults with underweight, normal weight, and overweight/obesity were 5.2 (3.1-8.8, I2 = 98.9%, n = 11 studies), 3.3 (2.6-4.3, I2 = 99.5%, n = 55 studies), and 11.5 (10.0-13.2, I2 = 99.7%, n = 69 studies) cases per 1000 person-years, respectively (Figure S2, panels A-C in the Online Supplementary Document).

Figure 1.  The pooled incidence of diabetes by baseline weight status. Panel A. Underweight. Panel B. Normal weight. Panel C. Overweight/obesity. BMI – body mass index (kg/m2), F – female, M – male.

Subgroup and meta-regression analysis of diabetes incidence

Subgroup (Table 1) and univariate meta-regression (Table 2) analyses indicated that diabetes incidence (per 1000 person-years) varied by age (older adults had a higher diabetes incidence), weight assessment (direct measurement had a higher diabetes incidence than self-report), and diabetes ascertainment methods (blood test and multiple had a higher diabetes incidence than medical records and self-report). Additionally, diabetes incidence in LMICs was significantly lower than that in HICs for adults with underweight (1.7 vs 7.6, odds ratio (OR) = 0.23; 95% CI = 0.09-0.56), while diabetes incidence in LMICs was significantly higher than that in HICs for adults with normal weight (5.8 vs 2.0, OR = 2.95; 95% CI = 1.87-4.54) and overweight/obesity (15.9 vs 8.9, OR = 1.78; 95% CI = 1.35-2.33). Compared with the Western Pacific, the Americas and European had a significantly lower diabetes incidence, while the South-East Asian Region and Eastern Mediterranean Region had similar diabetes incidences in adults with normal weight and overweight/obesity. Among the studies that reported sex-specific diabetes incidence in adults with normal weight, diabetes incidence did not differ between males and females. Studies with a follow-up duration ≤8 years reported a higher diabetes incidence (12.0, 95% CI = 10.2-14.2) than those with >8 years of follow-up (8.6, 95% CI = 7.6-9.8) for only overweight/obese adults.

Table 1.  Subgroup analysis of diabetes incidence by different weight status*

WordPress Data Table

CI – confidence interval, WHO – World Health Organization

*We defined weight status by the ethnic-specific body mass index classification recommended by the WHO [22].

†We categorised country income by the World Bank Country and Lending Groups in 2019 [26].

‡We categorised follow-up by its median.

§We defined multiple as blood test plus other ascertainment method.

Table 2.  Univariate meta-regression analysis of diabetes incidence by different weight status

WordPress Data Table

CI – confidence interval, OR – odds ratio, Ref – reference group, WHO – World Health Organization

*We defined weight status by the ethnic-specific body mass index classification recommended by the WHO [22].

†We categorised country income by the World Bank Country and Lending Groups in 2019 [26].

‡We defined multiple as blood test plus other ascertainment method.

The temporal trends of diabetes incidence by weight status

The pooled diabetes incidence (n = 14 studies) showed a sharp decrease since 1995 in adults with underweight (Figure 2). However, the diabetes incidence in adults with normal weight (n = 73 studies) increased continuously from 1.2 per 1000 person-years between 1985 and 1990 to 5.6 per 1000 person-years after 2010, with an estimated increase of 36% every five years. The pooled diabetes incidence in adults with overweight/obesity (n = 91 studies) showed a great increase from around six per 1000 person-years in 1985 to 11 per 1000 person-years between 1995 and 2000, remained stable between 2000 and 2010, but spiked to 16.4 per 1000 person-years after 2010. The bubble plot of diabetes incidence by median year of data collection indicated that the adjusted and unadjusted trends were significant across different weight statuses (Figure S3, panel A-C in the Online Supplementary Document). WHO region, country income, and diabetes ascertainment methods were not included as adjusting factors because of substantial correlation coefficients (>0.5) (Table S5 in the Online Supplementary Document). The results of sensitivity analyses by excluding studies with a NOS<7 in meta-regression analyses were similar to the primary analyses (Figure S4, panel A-C in the Online Supplementary Document).

Figure 2.  The temporal trends of diabetes incidence by baseline weight status. Blue line: Underweight. Red line: Normal weight. Green line: Overweight/obesity. The diabetes incidence was pooled by five years interval; median year of data collection was calculated by the mean value of baseline and follow-up year.

Prediabetes incidence by weight status

Three studies reported prediabetes and diabetes separately [103,113,121], while two reported hyperglycaemia (prediabetes combined with diabetes, but with diabetes cases only accounting for about 10%) (Table S6 in the Online Supplementary Document) [116,117]. All eligible studies were published between 2016 and 2021; they involved 93 910 adults and identified 19 758 cases during a median follow-up of 4.4 years (IQR = 3.5-8). The average prediabetes incidences for normal weight, normal weight combined overweight, and overweight/obesity were 47.4 (n = 3, range = 25.9-60.4), 46.3 (n = 2, range = 8-80.2), and 63.1 (n = 5, range = 10.0-95.9) per 1000 person-years, respectively.

Publication bias

Funnel plots for the pooled incidence in underweight and overweight/obesity (Figure S5, panel A-C in the Online Supplementary Document) showed an asymmetrical distribution, and the Egger regression tests were insignificant (P = 0.59 for underweight and P = 0.42 for overweight/obesity), while the results for normal weight adults were significant (P = 0.04).


We identified 94 prospective cohort studies containing data on the incidence of diabetes/prediabetes among approximately 3.4 million adults across varying weight statuses from 22 countries. This is the first meta-analysis to estimate the incidence of diabetes/prediabetes based on baseline weight status, which provides important insights for public health and clinical policies on diabetes prevention and management. The pooled incidences of diabetes in adults with underweight, normal weight, and overweight/obesity were 4.5, 2.7, and 10.5 per 1000 person-years, respectively. The incidence of prediabetes was higher than the incidence of diabetes, with averages of 47.4 in adults with normal weight and 63.1 per 1000 person-years in those with overweight/obesity. Moreover, this is the first study to assess the temporal trends in diabetes incidence by weight status. We found that diabetes incidence in adults with normal weight increased continuously from 1985 to 1990 with an estimated increase of 36% every five years. Diabetes incidence in adults with overweight/obesity sharply increased between 1995 and 2000 and then spiked 2010, while it greatly decreased in adults with underweight between 1995 and 2000 and between 2005 and 2010. Additionally, the pooled diabetes incidence varied by age, sex, country income, WHO region, study setting, weight assessment, and diabetes ascertainment methods.

Previous research has shown contradictory findings for trends of diabetes incidence. An aggregation analysis reported a stabilising or declining trend from 2010 onwards in many HICs [9], while the data from the Global Burden of Disease Study identified an increasing trend between 1990 and 2017 [128]. Although the two studies consistently used contemporary, real-world data over time to estimate the trends of diagnosed diabetes, they had no information on body weight. Our study is the first meta-analysis to assess the temporal trends of diabetes incidence by weight status, helping resolve this discrepancy. We found a significant increase in adults with overweight/obesity between 1995 and 2000 and after 2010. The temporal trends of diabetes in adults with underweight and overweight/obesity paralleled the trends of diabetes prevalence in those with underweight and overweight/obesity, respectively, reported by WHO during the same time period [129]. A shift in diet (traditional plant-based diet transitioned to more animal-based diet) caused by increasing economic status may be a potential explanation [130]. Animal-based diets provide sufficient nutrients to improve malnutrition status and excessive calories to increase obesity prevalence simultaneously [130]. Likewise, the temporal trends of diabetes incidence between 2000 and 2010 remained stable, as suggested by data from individual countries [11,131].

Importantly, we found that the temporal trend of diabetes incidence in adults with normal weight at baseline has increased robustly since 1985, with an estimated increase of 36% every five years, which contrasts stabilising trends of diabetes prevalence among adults with normal weight during recent decades [35]. This stabilising trend of diabetes prevalence may be caused by improved survival and declining mortality. Accumulating evidence has reported that the burden of diabetes among individuals with normal weight has increased, particularly in the Asian population [2,4,5]. Moreover, normal-weight individuals who develop diabetes may have a higher level of diabetes risk factors, including former smokers, hypertension, and physical inactivity [132]. Overweight/obesity has long been regarded as the critical parameter for diabetes control and management [16] and obesity is the primary focus in diabetes screening and intervention guidelines [12]. Our findings suggest that current diabetes screening policies and prevention strategies should not neglect non-overweight diabetes. More research to identify the metabolic differences between overweight and non-overweight diabetes, and the risk factors and effective prevention strategies for normal-weight diabetes is needed.

Moreover, our study confirmed that diabetes incidence in LMICs was significantly higher than that in HICs in adults with normal weight or overweight/obesity. This implies that the difference in diabetes prevalence between LMICs and HICs could be due to an increase in new diabetes cases in LMICs. However, the finding that LMICs had a lower diabetes incidence than HICs in underweight individuals is surprising. Underweight diabetes, as an atypical phenotype, may not fit into the classical definition of type 1 or type 2 diabetes well. However, its characteristics are commonly misdiagnosed as type 1 diabetes, particularly among lean individuals with deprived socioeconomic status in African and Asian countries [133]. The regional difference showed that the diabetes incidence in Americas and European regions was much lower than in the Western Pacific and South-East Asia. Apart from the economic level, ethnicity could also explain this regional difference [2]. The Asian population has a much lower BMI threshold for similar diabetes risk than the non-Asian population [22,134]. One of the strengths of our study is that we defined weight status using the ethnic-specific BMI classification [22]. These findings suggest that more efforts are needed to cope with the burden of diabetes in LMICs and non-Western countries.

Prediabetes is an intermediate metabolic state in the development of diabetes [135]. Evidence has shown that up to 70% of those with prediabetes will eventually progress to diabetes [12,135,136]. We found that the incidence of prediabetes was much higher than diabetes incidence even though the median follow-up period was shorter (four vs eight years). Importantly, the incidence of prediabetes was also high among adults with normal weight. Mapping the incidence of prediabetes is critical to predicting future trends of diabetes incidence. Currently, the data on prediabetes incidence stratified by weight status is limited, and further surveys are required.


Our study has several limitations. First, we filtered the search results to English-only studies and likely excluded those in other languages. However, we have tried to re-run title/abstract and full-text screening to include articles published in other languages; the newly identified articles did not affect the main results. Second, although assessing the temporal trends by using the median year of data collection is a widely used method [2830], it fails to obtain an accurate yearly incidence of diabetes and estimate the secular trends of diabetes incidence. Third, there was substantial heterogeneity of the pooled diabetes/prediabetes incidences by weight status. We have conducted subgroup analyses and meta-regression to explore the source of this heterogeneity and identified that the WHO region and weight and diabetes assessment methods were considerable sources of heterogeneity. However, some other important factors that may play a critical role in the diabetes development but are difficult to extract were not considered, including ethnicity [137], family history [134], and dietary pattern [138]. Fourth, although we included diabetes ascertainment methods as a potential subgroup, diabetes determined by self-reporting and medical records cannot distinguish types 1 and 2 diabetes. However, the bias caused by type 1 diabetes might be small, because over 95% diabetic patients have type 2 diabetes [19]. Finally, we defined weight status only by BMI, without considering other anthropometric parameters, including waist circumstance, fat distribution, or visceral fat. Moreover, we categorised weight status by baseline BMI, without consideration of weight change during follow-up. Therefore, these findings should be interpreted cautiously, and more accurate and timely data are needed to assess the secular trends of diabetes incidence by weight status.


We comprehensively assessed diabetes/prediabetes incidence across different weight statuses and their temporal trends. We found that the temporal trends of diabetes incidence for adults with overweight/obesity increased greatly, and while it showed a steady increase for those with normal weight, with an estimated increase of 36% every five years. More tailored prevention and intervention strategies and awareness campaigns to target non-overweight diabetes are required, particularly in LMICs and non-Western countries.

Additional material

Online Supplementary Document


All authors would like to thank the consultant service provided by the medical librarian of the University of Hong Kong when designing the search strategy among different databases.

Data availability: Application for datasets generated during and/or analyzed during the current study may be considered by the corresponding author on reasonable request.

[1] Funding: Publication made possible in part by support from the HKU Libraries Open Access Author Fund sponsored by the HKU Libraries.

[2] Authorship contributions: Hong-jie Yu: Conceptualisation, Methodology, Software, Literature screening, Data extraction, Formal analysis, Visualisation, Interpretation, Writing – Original Draft, Writing – review & editing. Mandy Ho: Conceptualisation, Methodology, Data curation, Supervision, Interpretation, Writing – Review and Editing. Xiangxiang Liu: Methodology, Literature screening, Data extraction, Formal analysis. Jundi Yang: Literature screening, Data extraction, Formal analysis. Pui Hing Chau: Supervision, Writing – Review and Editing. Daniel Yee Tak Fong: Supervision, Writing – Review and Editing.

[3] Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.


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Correspondence to:
Mandy Ho
School of Nursing, University of Hong Kong
5/F 3 Sassoon Road, Pokfulam, Hong Kong
[email protected].