Hypoglycemia in older adults with type 2 diabetes mellitus: Prevalence and associated risk factor: A systematic review and meta-analysis

Abstract

Background

Currently, hypoglycemia poses a critical challenge in managing older patients with type 2 diabetes mellitus (T2DM), threatening their long-term health and overall well-being. This meta-analysis is the first to comprehensively quantify the pooled prevalence of hypoglycemia and identify its key influencing factors specifically in the elderly population.

Methods

We conducted a systematic study across 11 databases to identify relevant observational studies reporting the prevalence of hypoglycemia or its influencing factors. The search was limited from database inception all the way until July 9th, 2025, and updated November 13th, 2025. In addition, we conducted a meta-analysis of the overall prevalence of hypoglycemia and its influencing factors using Stata 17.0 and RevMan 5.4, respectively.

Results

Among the 37 studies included, the overall prevalence of hypoglycemia in elderly patients with T2DM was 21% (95%CI, 20–23%). The results of this meta-analysis showed that age, diabetes duration, insulin treatment, hypertension, malignant tumors, renal impairment, cognitive dysfunction, and glycosylated hemoglobin were the key determinants influencing the risk of hypoglycemia in the elderly T2DM population.

Conclusion

Early clinical risk assessment is essential for older adults with type 2 diabetes mellitus at high risk of hypoglycemic complications, along with the implementation of evidence-based diagnostic and therapeutic protocols for hypoglycemia management.

Resumen

Antecedentes

Actualmente, la hipoglucemia representa un reto crítico en el manejo de los pacientes mayores con diabetes mellitus tipo 2 (DM2), ya que amenaza su salud a largo plazo y su bienestar general. Este metaanálisis es el primero en cuantificar exhaustivamente la prevalencia global de la hipoglucemia e identificar sus factores influyentes clave en la población de edad avanzada.

Métodos

Se realizó una búsqueda sistemática en 11 bases de datos para identificar estudios observacionales relevantes que informaran la prevalencia de hipoglucemia o sus factores influyentes. La búsqueda abarcó desde el inicio de cada base de datos hasta el 9 de julio de 2025, y se actualizó el 13 de noviembre de 2025. El metaanálisis de la prevalencia global de hipoglucemia y de sus factores influyentes se realizó mediante Stata 17.0 y RevMan 5.4, respectivamente.

Resultados

De los 37 estudios incluidos, la prevalencia global de hipoglucemia en pacientes ancianos con DM2 fue del 21% (IC 95%, 20%-23%). Los resultados de este metaanálisis mostraron que la edad, la duración de la diabetes, el tratamiento con insulina, la hipertensión, los tumores malignos, la insuficiencia renal, la disfunción cognitiva y la hemoglobina glicosilada eran los determinantes clave del riesgo de hipoglucemia en esta población.

Conclusión

Es necesario realizar una evaluación clínica temprana del riesgo en pacientes ancianos con DM2 con alto riesgo de complicaciones hipoglucémicas, así como establecer e implementar protocolos diagnósticos y terapéuticos basados en la evidencia para el manejo de la hipoglucemia.

Introduction

Type 2 diabetes mellitus (T2DM) is a common chronic metabolic disease, characterized by insulin resistance and impaired insulin secretion.¹ Epidemiological studies have indicated that 529 million people with diabetes globally in 2021, projected to reach 1.31 billion by 2050, with T2DM including more than 90% of cases.^2,3 Of note, with the background of accelerating global population aging and ongoing lifestyle transformations, the elderly population has gradually become the primary prevalent population of T2DM.⁴

Hypoglycemia is a prevalent acute complication that significantly threatens both the safety and quality of life of older patients with T2DM. Additionally, older adults are more susceptible to severe hypoglycemia than younger patients due to their reduced neurological responsiveness.⁵ Current evidence indicates that neuronal damage or cognitive dysfunction from a single severe hypoglycemic episode may negate the benefits of long-term glycemic control.⁶ Elderly patients with T2DM have a significantly elevated risk of asymptomatic hypoglycemia, and recurrent hypoglycemia significantly increases cardiovascular risk. In severe cases, it can directly cause irreversible cerebral dysfunction and a statistically significant increase in all-cause mortality.^7,8

Current clinical evidence indicates that drug-induced hypoglycemia is a major concern in the management of T2DM, primarily due to inappropriate dosing of oral hypoglycemic drugs or insulin administration.⁹ Elderly patients with T2DM frequently suffer from multiple comorbidities and consequently experience polypharmacy, wherein drug–drug interactions significantly increase the risk of hypoglycemia.¹⁰ Moreover, age-related physiological decline, particularly hepatic and renal dysfunction, prolongs the metabolic clearance of hypoglycemic medications. This delay leads to drug accumulation and impairs regulatory mechanisms against hypoglycemia.¹¹

Despite substantial research conducted globally on hypoglycemic events in elderly patients with T2DM, evidence gaps persist regarding hypoglycemia specifically in elderly patients with T2DM. Despite its clinical significance as a major challenge to quality of life and health care, the unique burden of hypoglycemia in this population has not been comprehensively addressed in existing systematic reviews. Therefore, this meta-analysis evaluated the pooled prevalence of hypoglycemia and identified key associated factors in older adults with type 2 diabetes mellitus. The findings aim to provide scientific evidence for implementing targeted prevention strategies in high-risk populations, ultimately enhancing health outcomes and quality of life for elderly patients with T2DM.

Materials and methods

The review protocol was registered with PROSPERO (CRD420251111622) in full compliance with the PRISMA statement. Registration was completed following the database searches but before the screening and data extraction phases, which is consistent with platform regulations. Future efforts should emphasize the need for prospective registration to ensure transparency and standardization in the review process.

Search strategy

We conducted a systematic literature search across 11 databases, including PubMed, Embase, Web of Science, The Cochrane Library, CINAHL, Medline, PsycInfo, CBM, CNKI, Wanfang, and VIP Database to identify studies reporting the prevalence of hypoglycemia or its influencing factors in elderly patients with T2DM. The search was limited from database inception all the way through July 9th, 2025, and updated November 13th, 2025. The search strategy used a combination of MeSH terms and free terms, utilizing Boolean operators (“AND”, “OR”) to construct the following search algorithm: (diabetes mellitus OR type 2 diabetes mellitus OR type 2 diabetes OR diabetes) AND (hypoglycemia OR blood glucose) AND (aged OR older OR elder OR elderly OR geriatric OR senile) AND (prevalence OR incidence OR occurrence OR epidemiology OR status OR risk* OR factor* OR predictor* OR determinant*). Furthermore, manual reference tracking of all included studies was conducted to ensure comprehensive coverage and minimize potential omission of eligible studies. Supplementary data presents the whole search strategy.

Selection criteria

This study rigorously followed the PECOS criteria (population, exposure, comparison, outcomes, study design) to establish the inclusion and exclusion criteria.

Inclusion criteria were: (1) study population should be older adults aged ≥ 60 years with clinically confirmed T2DM; (2) studies explicitly reported diagnostic criteria for both T2DM and hypoglycemia, with hypoglycemia defined as biochemically random or fasting plasma glucose levels ≤ 3.9 mmol/L (≤70 mg/dL) (and not by self-report)¹²; (3) reported outcomes included either the prevalence of hypoglycemia in elderly patients with T2DM or its influencing factors, with provided odds ratios (ORs) and 95% confidence intervals (CIs); (4) study design should be limited to observational methodologies, including cohort studies, case–control studies, and cross-sectional studies. Inclusion criteria based on the PECOS criteria are shown in Supplementary Table.

Exclusion criteria were: (1) non-observational study designs such as randomized controlled trials, reviews, systematic reviews, meta-analyses, case reports, or conference abstracts; (2) unavailable full-text publications or studies with incomplete data extraction; (3) duplicate publications by the same team; (4) low-quality literature with high risk of bias; (5) non-English or non-Chinese language literature.

Study screening and data extraction

All literature was imported into EndNote X9 software for management, and 2 researchers performed independent screening and data extraction to finalize the included studies based on the inclusion and exclusion criteria. In cases of discrepancies of the included studies, a 3rd researcher was consulted to discuss and reach a consensus. The data and information extracted included the first author's name, the publication year, country, study population, study design, study time, sample size, mean age, the prevalence of hypoglycemia and its influencing factors, such as including age, diabetes duration, woman, body mass index (BMI), insulin therapy, taking sulfonylureas, taking metformin, oral antidiabetic drug (OAD) combined with insulin, combined use of cardiovascular drugs, hypertension, malignant tumors, renal impairment, hepatic impairment, cognitive dysfunction, dyslipidemia, glycosylated hemoglobin (HbA1c), serum creatinine, abnormal blood leukocyte counts, drinking history).

Risk of bias and quality assessment

The methodological quality and risk of bias assessment for included studies were independently conducted by 2 researchers according to the study design, with discrepancies resolved through consultation with a 3rd researcher. Cross-sectional studies were evaluated using the 11-item quality assessment tool recommended by the Agency for Health care Research and Quality (AHRQ),¹³ with total scores categorized as follows: 0–3 was categorized as low-quality literature; 4–7 as moderate-quality literature; and 8–11 as high-quality literature. Cohort studies and case–control studies were evaluated employing the Newcastle–Ottawa Scale (NOS),¹⁴ which assessed 3 critical domains (sample selection, comparability, and outcome assessment), with a total score of 9, <4 being low-quality literature, 4–6 being moderate-quality literature, and ≥7 being high-quality literature. Studies demonstrating a high risk of bias (AHRQ or NOS score < 4) were systematically excluded from this analysis.

Statistical analysis

A single-group meta-analysis of prevalence was performed using Stata 17.0 to estimate the pooled prevalence of hypoglycemia and its 95%CI. The meta-analysis of influencing factors was performed in RevMan 5.4 to explore significant risk factors for hypoglycemic events. Heterogeneity of the included studies was assessed by Cochran's Q (P-value) and I². A random effects model was used when P < 0.05 and I² > 50% indicated significant heterogeneity; otherwise, a fixed effects model was used. For studies demonstrating substantial heterogeneity, subgroup analyses and meta-regression were conducted to explore potential sources of heterogeneity. Furthermore, the risk of publication bias was assessed by funnel plots, Egger's test, and Begg's test. The stability of the combined effect sizes was assessed by sensitivity analysis methods. Statistical significance of this meta-analysis was set at P < 0.05.

Results

Search process

The initial database search yielded a total of 7596 potentially relevant literature. Following duplicate removal, 4171 records underwent title and abstract screening, of which 308 proceeded to full-text evaluation. According to the predetermined inclusion and exclusion criteria and after cross-checking the screening results, 37 studies were ultimately included for the meta-analysis. Fig. 1 illustrates the specific process of screening studies with detailed reasons for exclusion.

Figure 1.

PRISMA flow diagram of study selection.

Study characteristics and quality assessment

A total of 37 studies including 327,333 elderly patients with T2DM were included, and the number of hypoglycemia cases in this population was 19,183. Among these studies, 36 reported the prevalence of hypoglycemia in elderly patients with T2DM,^15,33–51 and 17 studies examined associated influencing factors.^{16,21–25,27,29–32,35,36,42–44} All samples were from 17 countries worldwide, and these studies were conducted in Asia (n = 23),^{15,22–27,29,30,33–37,41–44,47,48} Europe (n = 8),^{20,28,38,45,49–51} and the Americas (n = 6),^{19,31,32,39,40,46} with Asian studies representing most of the included studies. The risk of bias was evaluated using NOS for 12 cohort studies ^{19,28,32,33,38–41,43,46,47,49} and 11 case–control studies,^{15,17,22–24,26,29–31,35,36} while AHRQ was used for assessing 14 cross-sectional studies.^{16,18,20,21,25,27,34,37,42,44,45,48,50,51} All included studies were evaluated as medium- or high-quality literature without high risk of bias. The basic characteristics of the included studies, along with detailed quality assessment outcomes are shown in Table 1.

Table 1.

The main characteristics and quality assessment of the included studies in the meta-analysis.

Study	Country	Population	Study design	Sample size	Age	Prevalence (%)	Influencing factors	Quality score
Chen 2015^15	China	Hospitalized	Case–control	200	68.90 ± 1.40	31.00	–	7
Hou 2024^16	China	Hospitalized	Cross-sectional	585	67.68 ± 6.21	32.99		7
Huang 2024^17	China	Hospitalized	Case–control	197	69.23 ± 5.40	41.62		8
Liu 2016^18	China	Community-dwelling	Cross-sectional	163	68.70 ± 3.80	13.50	–	8
Escalada 2015^19	United States	Databases	Retrospective cohort	31,035	≥60	9.88	–	7
Feinkohl 2014^20	United Kingdom	Outpatients	Cross-sectional	831	67.69 ± 4.16	10.23	–	7
Borzì 2016^21	Italy	Hospitalized	Cross-sectional	3167	75.20 ± 11.20	12.16		6
Liu 2024^22	China	Hospitalized	Case–control	545	≥60	33.40		6
Wang 2025^23	China	Outpatients	Case–control	320	69.49 ± 6.52	19.06		6
Zhang 2015^24	China	Hospitalized	Case–control	310	67.50 ± 5.30	25.81		7
Huang 2016^25	China	Hospitalized	Cross-sectional	137	72.20 ± 11.20	24.09		5
Zhang 2017^26	China	Hospitalized	Case–control	120	69.60 ± 3.70	33.33	–	7
AI-Azayzih2024^27	Saudi Arabia	Outpatients	Cross-sectional	640	67.19 ± 5.69	21.71		6
Bordier 2015^28	France	Community-dwelling	Cohort	987	77 ± 5	33.64	–	7
Kanazawa 2022^29	Japan	Hospitalized	Case–control	606	≥60	25.08		8
Ishikawa 2018^30	Japan	Outpatients	Case–control	170	74.10 ± 6.90	42.35		8
Alva-Cabrera 2021^31	Peru	Outpatients	Case–control	240	≥60	48.75		6
Kabue 2019^32	United States	Databases	Retrospective cohort	120,256	74.30 ± 6.80	–		7
Kimura 2021^33	Japan	Hospitalized	Cohort	1754	70–79	5.02	–	8
Shi 2022^34	Singapore	Community-dwelling	Cross-sectional	92	≥65	15.21	–	8
Ding 2016^35	China	Community-dwelling	Case–control	396	70.60 ± 1.20	30.05		7
Du 2013^36	China	Hospitalized	Case–control	306	68.50 ± 6.60	25.50		7
Nassar 2016^37	Iraq	Community-dwelling	Cross-sectional	116	≥60	74.14	–	7
Schloot 2016^38	Germany/Austria	Databases	Retrospective cohort	29,485	62.20–77.80	3.03	–	6
Moffet 2023^39	USA	Databases	Retrospective cohort	2158	≥65	3.66	–	6
Mehta 2017^40	USA	Databases	Retrospective cohort	53,055	75.50 ± 6.50	5.69	–	8
Chin 2016^41	Korea	Databases	Cohort	1957	67.50 ± 5.50	6.03	–	6
Zhang 2024^42	China	Hospitalized	Cross-sectional	546	78.06 ± 3.59	41.21		9
Chen 2022^43	China	Databases	Retrospective cohort	3877	77.50 ± 8.90	12.74		6
Akirov 2018^44	Israel	Hospitalized	Cross-sectional	4635	73 ± 13	17.09		7
Akin 2019^45	Turkey	Hospitalized	Cross-sectional	755	70.90 ± 5.90	23.31	–	7
Whitmer 2009^46	USA	Databases	Retrospective cohort	16,667	≥60	8.80	–	8
Kim 2020^47	Korea	Databases	Cohort	48,521	75.82 ± 5.41	12.30	–	7
Jayasekera 2025^48	Sri Lanka	Outpatients	Cross-sectional	895	≥65	28.49	–	8
Boureau 2023^49	France	Community-dwelling	Retrospective cohort	155	81.50 ± 5.30	37.42	–	6
Bo 2015^50	Italy	Community-dwelling	Cross-sectional	863	82.90 ± 2.10	6.60	–	7
Greco 2010^51	Italy	Hospitalized	Cross-sectional	591	84.70 ± 4.30	16.75	–	6

Note:

Age;

Diabetes duration;

Female;

BMI;

Insulin treatment;

Taking sulfonylureas;

Taking metformin;

OAD + insulin;

Combined use of cardiovascular drugs;

Hypertension;

Malignant tumors;

Renal impairment;

Hepatic impairment;

Cognitive dysfunction;

Dyslipidemia;

HbA1c;

Serum creatinine;

Abnormal blood leukocyte counts;

Drinking history.

Overall prevalence of hypoglycemia in older patients with type 2 diabetes mellitus

A meta-analysis was conducted on the prevalence of hypoglycemia in elderly patients with T2DM across 36 studies, with the overall prevalence ranging from 3.03% to 74.14%. The meta-analysis revealed significant heterogeneity across studies (I² = 99.5%, P < 0.001). Therefore, a random effects model was used for data synthesis, yielding the pooled prevalence of hypoglycemia of 21% (95%CI, 20–23%) in elderly patients with T2DM. The forest plot for the overall prevalence of hypoglycemia is shown in Fig. 2.

Figure 2.

Overall prevalence of hypoglycemia in elderly patients with type 2 diabetes mellitus.

Subgroup analysis and meta-regression

Due to the substantial heterogeneity across studies reporting the prevalence of hypoglycemia, this study explored the sources of heterogeneity through subgroup analysis. Table 2 illustrates the subgroup analysis results of the meta-analysis, which were pooled using a random effects model. Gender-stratified analysis revealed that the prevalence of hypoglycemia among elderly men with T2DM was 19% (95%CI, 17–21%), slightly higher than that among women (18% [95%CI, 16–21%]). Subgroup analyses by study population showed that the prevalence of hypoglycemia was 30% among community-dwelling patients, 28% among outpatients, 26% among hospitalized patients, and 8% in database-derived populations. Subgroup analysis based on study region showed that the overall prevalence of hypoglycemia in Asia was 26% (95%CI, 23–29%), which is significantly higher than 17% (95%CI, 11–24%) in Europe and 11% (95%CI, 9–14%) in the Americas. Subgroup analyses by study period showed that the prevalence of hypoglycemia was 27% in studies conducted in 2020 or later vs 19% in studies conducted before 2020. Although subgroup characteristics had a statistically significant impact on the prevalence of hypoglycemia in the studies, substantial heterogeneity persisted across the subgroups, suggesting that these subgroup characteristics were potential sources of heterogeneity between studies. Further meta-regression revealed that sample size was a significant source of heterogeneity in the effect size (β = 1.347; P = 0.001). The model explained 51.63% of the heterogeneity, but the substantial residual heterogeneity indicated the presence of other unaccounted sources. Due to insufficient data from included studies, subgroup analysis was not conducted for individual risk factors.

Table 2.

Subgroup analyses of the prevalence of hypoglycemia in elderly patients with type 2 diabetes mellitus.

Subgroup	No. studies	Heterogeneity	Merger effect Size
		I^2 (%)	Prevalence (%)	95%CI	Z-value	P-value
Overall prevalence	36	99.5	21	0.20–0.23	23.62	<0.001
Gender
Male	19	98.4	19	0.17–0.21	17.22	<0.001
Female	19	98.0	18	0.16–0.21	16.11	<0.001
Study population
Hospitalized patients	15	98.5	26	0.21–0.31	9.93	<0.001
Outpatients	6	97.9	28	0.18–0.38	5.50	<0.001
Community-dwelling patients	7	98.9	30	0.16–0.44	4.07	<0.001
Databases	8	99.8	8	0.05–0.10	5.72	<0.001
Study region
Asia	23	98.6	26	0.23–0.29	15.99	<0.001
Europe	8	99.3	17	0.11–0.24	5.45	<0.001
Americas	5	99.5	11	0.09–0.14	8.13	<0.001
Study time
Before 2020	26	99.5	19	0.17–0.21	19.44	<0.001
2020 and beyond	10	99.2	27	0.16–0.39	4.81	<0.001
Study design
Cross-sectional study	14	98.2	24	0.19–0.28	9.54	<0.001
Case–control study	11	89.7	32	0.27–0.37	12.95	<0.001
Prospective cohort study	4	99.4	14	0.08–0.20	4.57	<0.001
Retrospective cohort study	7	99.7	9	0.07–0.12	7.39	<0.001
Sample size
<1000	25	97.7	29	0.24–0.34	11.26	<0.001
≥1000	11	99.7	9	0.06–0.11	7.28	<0.001

Influencing factors of hypoglycemia in elderly patients with T2DM

The meta-analysis systematically evaluated a total of 19 potential influencing factors that were reported in at least 3 included studies. Results identified age (OR, 1.70; 95%CI, 1.10–2.63; P = 0.020), diabetes duration (OR, 1.69; 95%CI, 1.02–2.80; P = 0.040), insulin treatment (OR, 2.72; 95%CI, 1.84–4.03; P < 0.001), hypertension (OR, 1.67; 95%CI, 1.11–2.53; P = 0.010), malignant tumors (OR, 1.36, 95%CI, 1.03–1.79; P = 0.030), renal impairment (OR, 2.01; 95%CI, 1.34–3.02; P < 0.001) and cognitive dysfunction (OR, 1.23; 95%CI, 1.18–1.28; P < 0.001) were risk factors for the prevalence of hypoglycemia. Moreover, HbA1c (OR, 0.76; 95%CI, 0.63–0.93; P = 0.006) was a protective factor for it. The analysis results of the influencing factors are shown in Table 3.

Table 3.

Meta-analysis of influencing factors of hypoglycemia in elderly patients with type 2 diabetes mellitus.

Influencing factors	No. studies	Heterogeneity test results		Effect model	Merger effect size
		P-value	I^2 (%)		OR	95%CI	Z-value	P-value
Age	5	<0.001	94.0	Random	1.70	1.10–2.63	2.39	0.020*
Diabetes duration	9	<0.001	98.0	Random	1.69	1.02–2.80	2.05	0.040*
Female	4	<0.001	92.0	Random	0.88	0.53–1.47	0.47	0.640
BMI	6	<0.001	87.0	Random	1.10	0.64–1.89	0.35	0.730
Insulin treatment	9	<0.001	82.0	Random	2.72	1.84–4.03	5.01	<0.001*
Taking sulfonylureas	3	<0.001	86.0	Random	1.65	0.68–3.98	1.11	0.270
Taking metformin	5	0.410	0	Fixed	0.81	0.64–1.01	1.86	0.060
OAD + insulin	3	<0.001	90.0	Random	0.45	0.02–10.95	0.49	0.620
Combined use of cardiovascular drugs	3	0.610	0	Fixed	0.92	0.57–1.47	0.36	0.720
Hypertension	4	<0.001	89.0	Random	1.67	1.11–2.53	2.45	0.010*
Malignant tumors	3	0.120	53.0	Random	1.36	1.03–1.79	2.18	0.030*
Renal impairment	5	<0.001	89.0	Random	2.01	1.34–3.02	3.39	<0.001*
Hepatic impairment	4	0.005	77.0	Random	1.24	0.67–2.33	0.68	0.490
Cognitive dysfunction	3	0.560	0	Fixed	1.23	1.18–1.28	10.35	<0.001*
Dyslipidemia	3	<0.001	90.0	Random	1.30	0.45–3.76	0.48	0.630
HbA1c	6	<0.001	93.0	Random	0.76	0.63–0.93	2.72	0.006*
Serum creatinine	4	<0.001	87.0	Random	2.21	0.69–7.13	1.33	0.180
Abnormal blood leukocyte counts	3	<0.001	97.0	Random	0.16	0.01–2.68	1.28	0.200
Drinking history	3	0.020	75.0	Random	1.32	0.47–3.72	0.52	0.600

*

p-Value indicates statistical significance.

Random: Random effects model; Fixed: Fixed effects model.

Sensitivity analysis

Sensitivity analyses were conducted for both the prevalence of hypoglycemia and influencing factors with significant heterogeneity through sequential exclusion of individual studies. Sensitivity analysis showed that the overall prevalence of hypoglycemia in elderly patients with T2DM remained stable, as the combined effect size did not change substantially after removing any single study. This stability confirmed the reliability of the findings. Among them, the study conducted by Kabue et al.³² was identified as a significant source of heterogeneity in malignant tumors (OR, 1.51, 95%CI, 1.26–1.81, P < 0.001). Furthermore, sensitivity analyses were conducted on 8 statistically significant influencing factors in the combined effect sizes by alternating between the random effects model and fixed effects model. The results showed that there was no significant variation in either the ORs or corresponding 95%CIs after effect model transformation, suggesting that the results of the meta-analysis had good consistency and stability.

Publication bias

An assessment of publication bias was conducted for the 36 included studies reporting the prevalence of hypoglycemia. The funnel plot revealed a significant asymmetry in study distribution, which was confirmed by Egger's test (t = 4.86; P < 0.001), indicating the presence of publication bias. The trim and fill analysis for the overall prevalence of hypoglycemia revealed that the number of included studies increased from 36 to 44 after imputation. The pooled effect size was 1.176 (95%CI, 1.157–1.195). The confidence interval was entirely > 1 and excluded the null value, so the result was statistically significant (P < 0.001) (Fig. 3). Consequently, the pooled effect estimate showed no substantial alteration after employing the trim and fill method. This indicated that publication bias had a limited impact on the overall findings and that the study conclusions were robust and clinically meaningful. Due to the insufficient data of the included studies, the publication bias test was not performed for each influencing factor.

Figure 3.

The trim and fill analysis of the overall prevalence of hypoglycemia.

Discussion

This systematic review and meta-analysis included a total of 37 clinical studies comprising 327,333 elderly patients diagnosed with T2DM worldwide, aiming to quantitatively synthesize evidence on the prevalence of hypoglycemia and its influencing factors in this vulnerable population. Compared with a previous meta-analysis by Alwafi et al.,⁵² which reported a wide range of hypoglycemia prevalence in the general T2DM population (from 0.072 to 16,360 episodes per 1000 person-years), this meta-analysis specifically focused on the elderly subgroup and provided a more precise pooled prevalence estimate of 21%. In conclusion, these evidence-based findings provide critical guidance for developing targeted hypoglycemia prevention protocols and optimizing therapeutic strategies to enhance health outcomes in elderly populations with T2DM.

Among these, gender-stratified subgroup analysis revealed that the prevalence of hypoglycemia was slightly higher in elderly men vs women with T2DM. A study by Raparelli et al.⁵³ demonstrated that men have increased neuroendocrine sensitivity to hypoglycemic stimuli and are more likely to display poor self-management practices, resulting in an elevated risk of hypoglycemia. This highlights the need for gender-specific educational interventions focusing on medication adherence. Subgroup analysis based on study population revealed that community-dwelling elderly patients with T2DM had a significantly higher prevalence of hypoglycemia. Insufficient diabetes education for community-dwelling elderly patients leads to inadequate awareness of hypoglycemia risks and limited self-care capacity, thereby potentially increasing their vulnerability to hypoglycemia.⁵⁴ These findings strongly support the implementation of strengthened community-based diabetes management programs.

A critical finding was the pronounced geographical skew, with most studies originating from Asia. This limits the immediate global generalizability of the pooled prevalence estimate and warrants careful interpretation. Subgroup analysis based on study region revealed that elderly Asian populations had a higher prevalence of hypoglycemia vs cohorts from other regions. This may be attributed to generally higher insulin sensitivity in Asian populations and reduced tolerance to hypoglycemic medications, which is associated with their lower body weight and BMI.^55,56 Furthermore, this geographical distribution may reflect differential research focus across regions. The key influencing factors identified by this research have universal biological mechanisms, maintaining significant value for global clinical applications. While this concentration of evidence provides valuable insights for Asian populations, it necessitates caution when extrapolating these exact prevalence estimates to other regions. Future multinational studies are needed to establish more globally representative estimates.

As people get older, elderly patients with T2DM have experienced progressive deterioration of physiological functions and impaired drug metabolism.⁴ Additionally, this population is highly susceptible to impaired hypoglycemia awareness, elevating the risk of severe hypoglycemic episodes.^29,57 Therefore, regular functional and cognitive assessments should be incorporated into routine diabetes care for elderly patients. Furthermore, diabetes duration was a risk factor for hypoglycemia in elderly patients, which is consistent with the findings of Silbert et al.⁵⁸ As disease progresses, patients with T2DM experience gradual β-cell functional decline and dysregulation of counterregulatory hormonal responses, which underscores the importance of periodically re-evaluating treatment regimens in patients with long-standing diabetes. This meta-analysis showed that elderly patients with T2DM on insulin therapy have increased hypoglycemia susceptibility, which is supported by an initial insulin glargine trial indicating a 4.5 times higher hypoglycemia risk.⁵⁹ The clear clinical implication is that insulin initiation in elderly patients requires structured education programs, simplified regimens, and careful monitoring. In addition, Khunti et al.⁶⁰ indicated that elderly patients with T2DM use insulin for glycemic management. Prolonged diabetes duration and insulin use are associated with progressive β-cell dysfunction, thereby exacerbating hypoglycemia risk in this population.

Cognitive dysfunction is a significant risk factor for hypoglycemia in elderly patients with T2DM. Those with cognitive impairment have reduced disease self-management capacity, causing glycemic variability and severe hypoglycemia.⁶¹ This identifies that cognitive screening is crucial for diabetes care, and involving family or caregivers in management is essential for this vulnerable group. Additionally, HbA1c serves as a clinical indicator for evaluating long-term glycemic control in patients. For elderly patients with T2DM, maintaining HbA1c within the 7–8% range has been shown to optimally balance the reduction of diabetes-related complications with minimization of hypoglycemia risk.^62,63 This evidence strongly supports the adoption of less stringent glycemic targets in elderly patients as a direct strategy to reduce hypoglycemia risk. Notably, patients with T2DM with cognitive dysfunction and elevated HbA1c levels may experience significant blood glucose fluctuations. These metabolic instabilities can induce overexpression of proinflammatory cytokines, thereby exacerbating central nervous system disease, accelerating cognitive deterioration, and establishing a vicious cycle with recurrent hypoglycemia.⁶⁴

This study demonstrated that hypoglycemia in elderly patients with T2DM is significantly correlated with multiple comorbidities. First, renal function plays a crucial role in glucose homeostasis and drug metabolism. Compared with patients with normal renal function, elderly individuals with renal impairment exhibit an elevated risk of hypoglycemia.^65,66 Second, a large-scale longitudinal cohort study indicated that cardiovascular disease is a major risk factor for hypoglycemia in elderly patients with T2DM. The annual prevalence of severe hypoglycemic events demonstrates a progressive upward trend in this population.⁶⁷ Additionally, some malignant tumors secrete insulin-like growth factor (IGF) or insulin-like active substances, thus accelerating the hypoglycemic effect of insulin. Compounding this effect, patients with malignant tumors are in a hypermetabolic state with insufficient caloric intake, leading to rapid glucose consumption. When elderly patients have diminished glucose counterregulatory capacity, these factors synergistically increase vulnerability to hypoglycemic episodes.⁶⁸ In patients with malignancies, close nutritional support and frequent glucose monitoring are essential preventive measures that require collaboration between oncologists and endocrinologists. These findings suggest that health care providers should implement comprehensive patient education. Such programs should promote healthy lifestyle changes and integrate the management of multiple comorbidities with glycemic control, which will help mitigate associated health risks.¹⁶

Despite the strict inclusion criteria, which required biochemical confirmation of hypoglycemia (plasma glucose ≤ 3.9 mmol/L), methodological variations in glucose measurement existed across studies. Moreover, many included studies did not report their specific monitoring methods. Differences between self-monitored blood glucose (SMBG) and continuous glucose monitoring (CGM), for instance, may have contributed to the observed heterogeneity. Therefore, future studies would benefit from the development and use of standardized hypoglycemia assessment protocols.

Strengths and limitations

This study has several notable strengths. First, this study conducted a comprehensive search of relevant literature across multiple databases and systematically screened them based on the inclusion and exclusion criteria. Each included study underwent meticulous quality assessment, thereby substantially enhancing the reliability of the analytical outcomes and ensuring the overall methodological quality of this meta-analysis. In addition, this meta-analysis is the first comprehensive quantitative synthesis examining both the prevalence of hypoglycemia and its influencing factors in the elderly T2DM population. Data and conclusions drawn provide an evidence-based foundation for establishing targeted hypoglycemia management protocols and therapeutic goals, ultimately contributing to optimized clinical care for older patients with diabetes.

However, there are certain limitations in this study: (1) The heterogeneity of the pooled prevalence was significant. The potential sources were not fully explained by subgroup analyses and meta-regression, suggesting unmeasured clinical or methodological variations across studies. (2) The meta-analysis of risk factors was constrained by the limited number of eligible studies for certain variables, which affected the comprehensiveness of the evidence-based conclusions. (3) Although this provided concentrated evidence for a major global demographic, it limited the global generalizability of the pooled prevalence estimates. Future multinational studies are warranted to validate these findings across diverse ethnic and health care settings. (4) Although potential publication bias was detected, sensitivity analyses and the trim and fill method confirmed the robustness of the primary findings. Prospective registration and standardized reporting clinical practice guidelines are advised to build a more comprehensive evidence base. (5) Despite not affecting the overall validity of the evidence, the inclusion of only English and Chinese literature may have omitted some relevant data, causing a certain degree of language bias. Future studies should search across databases in different languages and establish multilingual teams to overcome language barriers.

Conclusions

In conclusion, this systematic review revealed that the prevalence of hypoglycemia in elderly patients with T2DM was 21%, with distinct variation patterns observed across subgroups stratified by different study characteristics. This high prevalence underscores the critical need for enhanced strategies for hypoglycemia prevention and management in this population, as well as for improved awareness of hypoglycemia among older adults. Although current evidence remains insufficient to establish comprehensive conclusions on this issue, according to the evidence-based results of this study, health care providers should pay attention to several relevant influencing factors, including age, diabetes duration, insulin therapy, hypertension, malignant tumors, renal impairment, cognitive dysfunction, and HbA1c. Targeted interventions addressing these factors may optimize patient safety and quality of life while advancing chronic disease management standards for older patients with T2DM.

Authors’ contributions

Yuelin Wang: Conceptualization, Formal analysis, Data curation, Software, Writing-original draft. Yunlan Jiang: Project administration, Supervision. Le Li: Methodology, Software. Hong Chen: Investigation, Visualization. Jing Wang: Investigation, Validation. Mengjie Zhang: Formal analysis, Data curation. Xiaoyu Bai: Data curation, Validation. Senlin Wu: Writing-review & editing.

Ethical approval

No human participants or animal subjects were involved in this study.

Funding

None declared.

Conflicts of interest

None declared.